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The case for economics — by the numbers

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In recent years, criticism has been levelled at economics for being insular and unconcerned about real-world problems. But a new study led by MIT scholars finds the field increasingly overlaps with the work of other disciplines, and, in a related development, has become more empirical and data-driven, while producing less work of pure theory.

The study examines 140,000 economics papers published over a 45-year span, from 1970 to 2015, tallying the “extramural” citations that economics papers received in 16 other academic fields — ranging from other social sciences such as sociology to medicine and public health. In seven of those fields, economics is the social science most likely to be cited, and it is virtually tied for first in citations in another two disciplines.

In psychology journals, for instance, citations of economics papers have more than doubled since 2000. Public health papers now cite economics work twice as often as they did 10 years ago, and citations of economics research in fields from operations research to computer science have risen sharply as well.

While citations of economics papers in the field of finance have risen slightly in the last two decades, that rate of growth is no higher than it is in many other fields, and the overall interaction between economics and finance has not changed much. That suggests economics has not been unusually oriented toward finance issues — as some critics have claimed since the banking-sector crash of 2007-2008. And the study’s authors contend that as economics becomes more empirical, it is less dogmatic.

“If you ask me, economics has never been better,” says Josh Angrist, an MIT economist who led the study. “It’s never been more useful. It’s never been more scientific and more evidence-based.”

Indeed, the proportion of economics papers based on empirical work — as opposed to theory or methodology — cited in top journals within the field has risen by roughly 20 percentage points since 1990.

The paper, “Inside Job or Deep Impact? Extramural Citations and the Influence of Economic Scholarship,” appears in this month’s issue of the Journal of Economic Literature .

The co-authors are Angrist, who is the Ford Professor of Economics in MIT Department of Economics; Pierre Azoulay, the International Programs Professor of Management at the MIT Sloan School of Management; Glenn Ellison, the Gregory K. Palm Professor Economics and associate head of the Department of Economics; Ryan Hill, a doctoral candidate in MIT’s Department of Economics; and Susan Feng Lu, an associate professor of management in Purdue University’s Krannert School of Management.

Taking critics seriously

As Angrist acknowledges, one impetus for the study was the wave of criticism the economics profession has faced over the last decade, after the banking crisis and the “Great Recession” of 2008-2009, which included the finance-sector crash of 2008. The paper’s title alludes to the film “Inside Job” — whose thesis holds that, as Angrist puts it, “economics scholarship as an academic enterprise was captured somehow by finance, and that academic economists should therefore be blamed for the Great Recession.”

To conduct the study, the researchers used the Web of Science, a comprehensive bibliographic database, to examine citations between 1970 and 2015. The scholars developed machine-learning techniques to classify economics papers into subfields (such as macroeconomics or industrial organization) and by research “style” —  meaning whether papers are primarily concerned with economic theory, empirical analysis, or econometric methods.

“We did a lot of fine-tuning of that,” says Hill, noting that for a study of this size, a machine-learning approach is a necessity.

The study also details the relationship between economics and four additional social science disciplines: anthropology, political science, psychology, and sociology. Among these, political science has overtaken sociology as the discipline most engaged with economics. Psychology papers now cite economics research about as often as they cite works of sociology.

The new intellectual connectivity between economics and psychology appears to be a product of the growth of behavioral economics, which examines the irrational, short-sighted financial decision-making of individuals — a different paradigm than the assumptions about rational decision-making found in neoclassical economics. During the study’s entire time period, one of the economics papers cited most often by other disciplines is the classic article “Prospect Theory: An Analysis of Decision under Risk,” by behavioral economists Daniel Kahneman and Amos Tversky.

Beyond the social sciences, other academic disciplines for which the researchers studied the influence of economics include four classic business fields — accounting, finance, management, and marketing — as well as computer science, mathematics, medicine, operations research, physics, public health, and statistics.

The researchers believe these “extramural” citations of economics are a good indicator of economics’ scientific value and relevance.

“Economics is getting more citations from computer science and sociology, political science, and psychology, but we also see fields like public health and medicine starting to cite economics papers,” Angrist says. “The empirical share of the economics publication output is growing. That’s a fairly marked change. But even more dramatic is the proportion of citations that flow to empirical work.”

Ellison emphasizes that because other disciplines are citing empirical economics more often, it shows that the growth of empirical research in economics is not just a self-reinforcing change, in which scholars chase trendy ideas. Instead, he notes, economists are producing broadly useful empirical research.  

“Political scientists would feel totally free to ignore what economists were writing if what economists were writing today wasn’t of interest to them,” Ellison says. “But we’ve had this big shift in what we do, and other disciplines are showing their interest.”

It may also be that the empirical methods used in economics now more closely match those in other disciplines as well.

“What’s new is that economics is producing more accessible empirical work,” Hill says. “Our methods are becoming more similar … through randomized controlled trials, lab experiments, and other experimental approaches.”

But as the scholars note, there are exceptions to the general pattern in which greater empiricism in economics corresponds to greater interest from other fields. Computer science and operations research papers, which increasingly cite economists’ research, are mostly interested in the theory side of economics. And the growing overlap between psychology and economics involves a mix of theory and data-driven work.

In a big country

Angrist says he hopes the paper will help journalists and the general public appreciate how varied economics research is.

“To talk about economics is sort of like talking about [the United States of] America,” Angrist says. “America is a big, diverse country, and economics scholarship is a big, diverse enterprise, with many fields.”

He adds: “I think economics is incredibly eclectic.”

Ellison emphasizes this point as well, observing that the sheer breadth of the discipline gives economics the ability to have an impact in so many other fields.  

“It really seems to be the diversity of economics that makes it do well in influencing other fields,” Ellison says. “Operations research, computer science, and psychology are paying a lot of attention to economic theory. Sociologists are paying a lot of attention to labor economics, marketing and management are paying attention to industrial organization, statisticians are paying attention to econometrics, and the public health people are paying attention to health economics. Just about everything in economics is influential somewhere.”

For his part, Angrist notes that he is a biased observer: He is a dedicated empiricist and a leading practitioner of research that uses quasiexperimental methods. His studies leverage circumstances in which, say, policy changes random assignments in civic life allow researchers to study two otherwise similar groups of people separated by one thing, such as access to health care.

Angrist was also a graduate-school advisor of Esther Duflo PhD ’99, who won the Nobel Prize in economics last fall, along with MIT’s Abhijit Banerjee — and Duflo thanked Angrist at their Nobel press conference, citing his methodological influence on her work. Duflo and Banerjee, as co-founders of MIT’s Abdul Latif Jameel Poverty Action Lab (J-PAL), are advocates of using field experiments in economics, which is still another way of producing empirical results with policy implications.

“More and more of our empirical work is worth paying attention to, and people do increasingly pay attention to it,” Angrist says. “At the same time, economists are much less inward-looking than they used to be.”

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Examiner and Judge Designs in Economics: A Practitioner's Guide

This article provides empirical researchers with an introduction and guide to research designs based on variation in judge and examiner tendencies to administer treatments or other interventions. We review the basic theory behind the research design, outline the assumptions under which the design identifies causal effects, describe empirical tests of those assumptions, and discuss tradeoffs associated with choices researchers must make for estimation. We demonstrate concepts and best practices concretely in an empirical case study that uses an examiner tendency research design to study the effects of pre-trial detention.

This manuscript is in preparation for the Journal of Economic Literature. Programs and data for the empirical case study will be provided online. We are grateful for insightful comments from David Romer and four anonymous referees. In addition, we received constructive feedback from Amanda Agan, Elior Cohen, Rob Collinson, Jason Cook, Gordon Dahl, John Eric Humphries, Lawrence Katz, Magne Mogstad, Samuel Norris, Aurelie Ouss, Roman Rivera, Henrik Sigstad, Kamelia Stavreva, Megan Stevenson, and Winnie van Dijk. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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• Published: 17 April 2024

The economic commitment of climate change

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Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons 1 , 2 , 3 , 4 , 5 , 6 . Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes 7 , 8 . Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11–29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

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Projections of the macroeconomic damage caused by future climate change are crucial to informing public and policy debates about adaptation, mitigation and climate justice. On the one hand, adaptation against climate impacts must be justified and planned on the basis of an understanding of their future magnitude and spatial distribution 9 . This is also of importance in the context of climate justice 10 , as well as to key societal actors, including governments, central banks and private businesses, which increasingly require the inclusion of climate risks in their macroeconomic forecasts to aid adaptive decision-making 11 , 12 . On the other hand, climate mitigation policy such as the Paris Climate Agreement is often evaluated by balancing the costs of its implementation against the benefits of avoiding projected physical damages. This evaluation occurs both formally through cost–benefit analyses 1 , 4 , 5 , 6 , as well as informally through public perception of mitigation and damage costs 13 .

Projections of future damages meet challenges when informing these debates, in particular the human biases relating to uncertainty and remoteness that are raised by long-term perspectives 14 . Here we aim to overcome such challenges by assessing the extent of economic damages from climate change to which the world is already committed by historical emissions and socio-economic inertia (the range of future emission scenarios that are considered socio-economically plausible 15 ). Such a focus on the near term limits the large uncertainties about diverging future emission trajectories, the resulting long-term climate response and the validity of applying historically observed climate–economic relations over long timescales during which socio-technical conditions may change considerably. As such, this focus aims to simplify the communication and maximize the credibility of projected economic damages from future climate change.

In projecting the future economic damages from climate change, we make use of recent advances in climate econometrics that provide evidence for impacts on sub-national economic growth from numerous components of the distribution of daily temperature and precipitation 3 , 7 , 8 . Using fixed-effects panel regression models to control for potential confounders, these studies exploit within-region variation in local temperature and precipitation in a panel of more than 1,600 regions worldwide, comprising climate and income data over the past 40 years, to identify the plausibly causal effects of changes in several climate variables on economic productivity 16 , 17 . Specifically, macroeconomic impacts have been identified from changing daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall that occur in addition to those already identified from changing average temperature 2 , 3 , 18 . Moreover, regional heterogeneity in these effects based on the prevailing local climatic conditions has been found using interactions terms. The selection of these climate variables follows micro-level evidence for mechanisms related to the impacts of average temperatures on labour and agricultural productivity 2 , of temperature variability on agricultural productivity and health 7 , as well as of precipitation on agricultural productivity, labour outcomes and flood damages 8 (see Extended Data Table 1 for an overview, including more detailed references). References  7 , 8 contain a more detailed motivation for the use of these particular climate variables and provide extensive empirical tests about the robustness and nature of their effects on economic output, which are summarized in Methods . By accounting for these extra climatic variables at the sub-national level, we aim for a more comprehensive description of climate impacts with greater detail across both time and space.

Constraining the persistence of impacts

A key determinant and source of discrepancy in estimates of the magnitude of future climate damages is the extent to which the impact of a climate variable on economic growth rates persists. The two extreme cases in which these impacts persist indefinitely or only instantaneously are commonly referred to as growth or level effects 19 , 20 (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for mathematical definitions). Recent work shows that future damages from climate change depend strongly on whether growth or level effects are assumed 20 . Following refs.  2 , 18 , we provide constraints on this persistence by using distributed lag models to test the significance of delayed effects separately for each climate variable. Notably, and in contrast to refs.  2 , 18 , we use climate variables in their first-differenced form following ref.  3 , implying a dependence of the growth rate on a change in climate variables. This choice means that a baseline specification without any lags constitutes a model prior of purely level effects, in which a permanent change in the climate has only an instantaneous effect on the growth rate 3 , 19 , 21 . By including lags, one can then test whether any effects may persist further. This is in contrast to the specification used by refs.  2 , 18 , in which climate variables are used without taking the first difference, implying a dependence of the growth rate on the level of climate variables. In this alternative case, the baseline specification without any lags constitutes a model prior of pure growth effects, in which a change in climate has an infinitely persistent effect on the growth rate. Consequently, including further lags in this alternative case tests whether the initial growth impact is recovered 18 , 19 , 21 . Both of these specifications suffer from the limiting possibility that, if too few lags are included, one might falsely accept the model prior. The limitations of including a very large number of lags, including loss of data and increasing statistical uncertainty with an increasing number of parameters, mean that such a possibility is likely. By choosing a specification in which the model prior is one of level effects, our approach is therefore conservative by design, avoiding assumptions of infinite persistence of climate impacts on growth and instead providing a lower bound on this persistence based on what is observable empirically (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for further exposition of this framework). The conservative nature of such a choice is probably the reason that ref.  19 finds much greater consistency between the impacts projected by models that use the first difference of climate variables, as opposed to their levels.

We begin our empirical analysis of the persistence of climate impacts on growth using ten lags of the first-differenced climate variables in fixed-effects distributed lag models. We detect substantial effects on economic growth at time lags of up to approximately 8–10 years for the temperature terms and up to approximately 4 years for the precipitation terms (Extended Data Fig. 1 and Extended Data Table 2 ). Furthermore, evaluation by means of information criteria indicates that the inclusion of all five climate variables and the use of these numbers of lags provide a preferable trade-off between best-fitting the data and including further terms that could cause overfitting, in comparison with model specifications excluding climate variables or including more or fewer lags (Extended Data Fig. 3 , Supplementary Methods Section  1 and Supplementary Table 1 ). We therefore remove statistically insignificant terms at later lags (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). Further tests using Monte Carlo simulations demonstrate that the empirical models are robust to autocorrelation in the lagged climate variables (Supplementary Methods Section  2 and Supplementary Figs. 4 and 5 ), that information criteria provide an effective indicator for lag selection (Supplementary Methods Section  2 and Supplementary Fig. 6 ), that the results are robust to concerns of imperfect multicollinearity between climate variables and that including several climate variables is actually necessary to isolate their separate effects (Supplementary Methods Section  3 and Supplementary Fig. 7 ). We provide a further robustness check using a restricted distributed lag model to limit oscillations in the lagged parameter estimates that may result from autocorrelation, finding that it provides similar estimates of cumulative marginal effects to the unrestricted model (Supplementary Methods Section 4 and Supplementary Figs. 8 and 9 ). Finally, to explicitly account for any outstanding uncertainty arising from the precise choice of the number of lags, we include empirical models with marginally different numbers of lags in the error-sampling procedure of our projection of future damages. On the basis of the lag-selection procedure (the significance of lagged terms in Extended Data Fig. 1 and Extended Data Table 2 , as well as information criteria in Extended Data Fig. 3 ), we sample from models with eight to ten lags for temperature and four for precipitation (models shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). In summary, this empirical approach to constrain the persistence of climate impacts on economic growth rates is conservative by design in avoiding assumptions of infinite persistence, but nevertheless provides a lower bound on the extent of impact persistence that is robust to the numerous tests outlined above.

Committed damages until mid-century

We combine these empirical economic response functions (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) with an ensemble of 21 climate models (see Supplementary Table 5 ) from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) 22 to project the macroeconomic damages from these components of physical climate change (see Methods for further details). Bias-adjusted climate models that provide a highly accurate reproduction of observed climatological patterns with limited uncertainty (Supplementary Table 6 ) are used to avoid introducing biases in the projections. Following a well-developed literature 2 , 3 , 19 , these projections do not aim to provide a prediction of future economic growth. Instead, they are a projection of the exogenous impact of future climate conditions on the economy relative to the baselines specified by socio-economic projections, based on the plausibly causal relationships inferred by the empirical models and assuming ceteris paribus. Other exogenous factors relevant for the prediction of economic output are purposefully assumed constant.

A Monte Carlo procedure that samples from climate model projections, empirical models with different numbers of lags and model parameter estimates (obtained by 1,000 block-bootstrap resamples of each of the regressions in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) is used to estimate the combined uncertainty from these sources. Given these uncertainty distributions, we find that projected global damages are statistically indistinguishable across the two most extreme emission scenarios until 2049 (at the 5% significance level; Fig. 1 ). As such, the climate damages occurring before this time constitute those to which the world is already committed owing to the combination of past emissions and the range of future emission scenarios that are considered socio-economically plausible 15 . These committed damages comprise a permanent income reduction of 19% on average globally (population-weighted average) in comparison with a baseline without climate-change impacts (with a likely range of 11–29%, following the likelihood classification adopted by the Intergovernmental Panel on Climate Change (IPCC); see caption of Fig. 1 ). Even though levels of income per capita generally still increase relative to those of today, this constitutes a permanent income reduction for most regions, including North America and Europe (each with median income reductions of approximately 11%) and with South Asia and Africa being the most strongly affected (each with median income reductions of approximately 22%; Fig. 1 ). Under a middle-of-the road scenario of future income development (SSP2, in which SSP stands for Shared Socio-economic Pathway), this corresponds to global annual damages in 2049 of 38 trillion in 2005 international dollars (likely range of 19–59 trillion 2005 international dollars). Compared with empirical specifications that assume pure growth or pure level effects, our preferred specification that provides a robust lower bound on the extent of climate impact persistence produces damages between these two extreme assumptions (Extended Data Fig. 3 ).

Estimates of the projected reduction in income per capita from changes in all climate variables based on empirical models of climate impacts on economic output with a robust lower bound on their persistence (Extended Data Fig. 1 ) under a low-emission scenario compatible with the 2 °C warming target and a high-emission scenario (SSP2-RCP2.6 and SSP5-RCP8.5, respectively) are shown in purple and orange, respectively. Shading represents the 34% and 10% confidence intervals reflecting the likely and very likely ranges, respectively (following the likelihood classification adopted by the IPCC), having estimated uncertainty from a Monte Carlo procedure, which samples the uncertainty from the choice of physical climate models, empirical models with different numbers of lags and bootstrapped estimates of the regression parameters shown in Supplementary Figs. 1 – 3 . Vertical dashed lines show the time at which the climate damages of the two emission scenarios diverge at the 5% and 1% significance levels based on the distribution of differences between emission scenarios arising from the uncertainty sampling discussed above. Note that uncertainty in the difference of the two scenarios is smaller than the combined uncertainty of the two respective scenarios because samples of the uncertainty (climate model and empirical model choice, as well as model parameter bootstrap) are consistent across the two emission scenarios, hence the divergence of damages occurs while the uncertainty bounds of the two separate damage scenarios still overlap. Estimates of global mitigation costs from the three IAMs that provide results for the SSP2 baseline and SSP2-RCP2.6 scenario are shown in light green in the top panel, with the median of these estimates shown in bold.

Damages already outweigh mitigation costs

We compare the damages to which the world is committed over the next 25 years to estimates of the mitigation costs required to achieve the Paris Climate Agreement. Taking estimates of mitigation costs from the three integrated assessment models (IAMs) in the IPCC AR6 database 23 that provide results under comparable scenarios (SSP2 baseline and SSP2-RCP2.6, in which RCP stands for Representative Concentration Pathway), we find that the median committed climate damages are larger than the median mitigation costs in 2050 (six trillion in 2005 international dollars) by a factor of approximately six (note that estimates of mitigation costs are only provided every 10 years by the IAMs and so a comparison in 2049 is not possible). This comparison simply aims to compare the magnitude of future damages against mitigation costs, rather than to conduct a formal cost–benefit analysis of transitioning from one emission path to another. Formal cost–benefit analyses typically find that the net benefits of mitigation only emerge after 2050 (ref.  5 ), which may lead some to conclude that physical damages from climate change are simply not large enough to outweigh mitigation costs until the second half of the century. Our simple comparison of their magnitudes makes clear that damages are actually already considerably larger than mitigation costs and the delayed emergence of net mitigation benefits results primarily from the fact that damages across different emission paths are indistinguishable until mid-century (Fig. 1 ).

Although these near-term damages constitute those to which the world is already committed, we note that damage estimates diverge strongly across emission scenarios after 2049, conveying the clear benefits of mitigation from a purely economic point of view that have been emphasized in previous studies 4 , 24 . As well as the uncertainties assessed in Fig. 1 , these conclusions are robust to structural choices, such as the timescale with which changes in the moderating variables of the empirical models are estimated (Supplementary Figs. 10 and 11 ), as well as the order in which one accounts for the intertemporal and international components of currency comparison (Supplementary Fig. 12 ; see Methods for further details).

Damages from variability and extremes

Committed damages primarily arise through changes in average temperature (Fig. 2 ). This reflects the fact that projected changes in average temperature are larger than those in other climate variables when expressed as a function of their historical interannual variability (Extended Data Fig. 4 ). Because the historical variability is that on which the empirical models are estimated, larger projected changes in comparison with this variability probably lead to larger future impacts in a purely statistical sense. From a mechanistic perspective, one may plausibly interpret this result as implying that future changes in average temperature are the most unprecedented from the perspective of the historical fluctuations to which the economy is accustomed and therefore will cause the most damage. This insight may prove useful in terms of guiding adaptation measures to the sources of greatest damage.

Estimates of the median projected reduction in sub-national income per capita across emission scenarios (SSP2-RCP2.6 and SSP2-RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ). a , Impacts arising from all climate variables. b – f , Impacts arising separately from changes in annual mean temperature ( b ), daily temperature variability ( c ), total annual precipitation ( d ), the annual number of wet days (>1 mm) ( e ) and extreme daily rainfall ( f ) (see Methods for further definitions). Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Nevertheless, future damages based on empirical models that consider changes in annual average temperature only and exclude the other climate variables constitute income reductions of only 13% in 2049 (Extended Data Fig. 5a , likely range 5–21%). This suggests that accounting for the other components of the distribution of temperature and precipitation raises net damages by nearly 50%. This increase arises through the further damages that these climatic components cause, but also because their inclusion reveals a stronger negative economic response to average temperatures (Extended Data Fig. 5b ). The latter finding is consistent with our Monte Carlo simulations, which suggest that the magnitude of the effect of average temperature on economic growth is underestimated unless accounting for the impacts of other correlated climate variables (Supplementary Fig. 7 ).

In terms of the relative contributions of the different climatic components to overall damages, we find that accounting for daily temperature variability causes the largest increase in overall damages relative to empirical frameworks that only consider changes in annual average temperature (4.9 percentage points, likely range 2.4–8.7 percentage points, equivalent to approximately 10 trillion international dollars). Accounting for precipitation causes smaller increases in overall damages, which are—nevertheless—equivalent to approximately 1.2 trillion international dollars: 0.01 percentage points (−0.37–0.33 percentage points), 0.34 percentage points (0.07–0.90 percentage points) and 0.36 percentage points (0.13–0.65 percentage points) from total annual precipitation, the number of wet days and extreme daily precipitation, respectively. Moreover, climate models seem to underestimate future changes in temperature variability 25 and extreme precipitation 26 , 27 in response to anthropogenic forcing as compared with that observed historically, suggesting that the true impacts from these variables may be larger.

The distribution of committed damages

The spatial distribution of committed damages (Fig. 2a ) reflects a complex interplay between the patterns of future change in several climatic components and those of historical economic vulnerability to changes in those variables. Damages resulting from increasing annual mean temperature (Fig. 2b ) are negative almost everywhere globally, and larger at lower latitudes in regions in which temperatures are already higher and economic vulnerability to temperature increases is greatest (see the response heterogeneity to mean temperature embodied in Extended Data Fig. 1a ). This occurs despite the amplified warming projected at higher latitudes 28 , suggesting that regional heterogeneity in economic vulnerability to temperature changes outweighs heterogeneity in the magnitude of future warming (Supplementary Fig. 13a ). Economic damages owing to daily temperature variability (Fig. 2c ) exhibit a strong latitudinal polarisation, primarily reflecting the physical response of daily variability to greenhouse forcing in which increases in variability across lower latitudes (and Europe) contrast decreases at high latitudes 25 (Supplementary Fig. 13b ). These two temperature terms are the dominant determinants of the pattern of overall damages (Fig. 2a ), which exhibits a strong polarity with damages across most of the globe except at the highest northern latitudes. Future changes in total annual precipitation mainly bring economic benefits except in regions of drying, such as the Mediterranean and central South America (Fig. 2d and Supplementary Fig. 13c ), but these benefits are opposed by changes in the number of wet days, which produce damages with a similar pattern of opposite sign (Fig. 2e and Supplementary Fig. 13d ). By contrast, changes in extreme daily rainfall produce damages in all regions, reflecting the intensification of daily rainfall extremes over global land areas 29 , 30 (Fig. 2f and Supplementary Fig. 13e ).

The spatial distribution of committed damages implies considerable injustice along two dimensions: culpability for the historical emissions that have caused climate change and pre-existing levels of socio-economic welfare. Spearman’s rank correlations indicate that committed damages are significantly larger in countries with smaller historical cumulative emissions, as well as in regions with lower current income per capita (Fig. 3 ). This implies that those countries that will suffer the most from the damages already committed are those that are least responsible for climate change and which also have the least resources to adapt to it.

Estimates of the median projected change in national income per capita across emission scenarios (RCP2.6 and RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ) are plotted against cumulative national emissions per capita in 2020 (from the Global Carbon Project) and coloured by national income per capita in 2020 (from the World Bank) in a and vice versa in b . In each panel, the size of each scatter point is weighted by the national population in 2020 (from the World Bank). Inset numbers indicate the Spearman’s rank correlation ρ and P -values for a hypothesis test whose null hypothesis is of no correlation, as well as the Spearman’s rank correlation weighted by national population.

To further quantify this heterogeneity, we assess the difference in committed damages between the upper and lower quartiles of regions when ranked by present income levels and historical cumulative emissions (using a population weighting to both define the quartiles and estimate the group averages). On average, the quartile of countries with lower income are committed to an income loss that is 8.9 percentage points (or 61%) greater than the upper quartile (Extended Data Fig. 6 ), with a likely range of 3.8–14.7 percentage points across the uncertainty sampling of our damage projections (following the likelihood classification adopted by the IPCC). Similarly, the quartile of countries with lower historical cumulative emissions are committed to an income loss that is 6.9 percentage points (or 40%) greater than the upper quartile, with a likely range of 0.27–12 percentage points. These patterns reemphasize the prevalence of injustice in climate impacts 31 , 32 , 33 in the context of the damages to which the world is already committed by historical emissions and socio-economic inertia.

Contextualizing the magnitude of damages

The magnitude of projected economic damages exceeds previous literature estimates 2 , 3 , arising from several developments made on previous approaches. Our estimates are larger than those of ref.  2 (see first row of Extended Data Table 3 ), primarily because of the facts that sub-national estimates typically show a steeper temperature response (see also refs.  3 , 34 ) and that accounting for other climatic components raises damage estimates (Extended Data Fig. 5 ). However, we note that our empirical approach using first-differenced climate variables is conservative compared with that of ref.  2 in regard to the persistence of climate impacts on growth (see introduction and Methods section ‘Empirical model specification: fixed-effects distributed lag models’), an important determinant of the magnitude of long-term damages 19 , 21 . Using a similar empirical specification to ref.  2 , which assumes infinite persistence while maintaining the rest of our approach (sub-national data and further climate variables), produces considerably larger damages (purple curve of Extended Data Fig. 3 ). Compared with studies that do take the first difference of climate variables 3 , 35 , our estimates are also larger (see second and third rows of Extended Data Table 3 ). The inclusion of further climate variables (Extended Data Fig. 5 ) and a sufficient number of lags to more adequately capture the extent of impact persistence (Extended Data Figs. 1 and 2 ) are the main sources of this difference, as is the use of specifications that capture nonlinearities in the temperature response when compared with ref.  35 . In summary, our estimates develop on previous studies by incorporating the latest data and empirical insights 7 , 8 , as well as in providing a robust empirical lower bound on the persistence of impacts on economic growth, which constitutes a middle ground between the extremes of the growth-versus-levels debate 19 , 21 (Extended Data Fig. 3 ).

Compared with the fraction of variance explained by the empirical models historically (<5%), the projection of reductions in income of 19% may seem large. This arises owing to the fact that projected changes in climatic conditions are much larger than those that were experienced historically, particularly for changes in average temperature (Extended Data Fig. 4 ). As such, any assessment of future climate-change impacts necessarily requires an extrapolation outside the range of the historical data on which the empirical impact models were evaluated. Nevertheless, these models constitute the most state-of-the-art methods for inference of plausibly causal climate impacts based on observed data. Moreover, we take explicit steps to limit out-of-sample extrapolation by capping the moderating variables of the interaction terms at the 95th percentile of the historical distribution (see Methods ). This avoids extrapolating the marginal effects outside what was observed historically. Given the nonlinear response of economic output to annual mean temperature (Extended Data Fig. 1 and Extended Data Table 2 ), this is a conservative choice that limits the magnitude of damages that we project. Furthermore, back-of-the-envelope calculations indicate that the projected damages are consistent with the magnitude and patterns of historical economic development (see Supplementary Discussion Section  5 ).

Missing impacts and spatial spillovers

Despite assessing several climatic components from which economic impacts have recently been identified 3 , 7 , 8 , this assessment of aggregate climate damages should not be considered comprehensive. Important channels such as impacts from heatwaves 31 , sea-level rise 36 , tropical cyclones 37 and tipping points 38 , 39 , as well as non-market damages such as those to ecosystems 40 and human health 41 , are not considered in these estimates. Sea-level rise is unlikely to be feasibly incorporated into empirical assessments such as this because historical sea-level variability is mostly small. Non-market damages are inherently intractable within our estimates of impacts on aggregate monetary output and estimates of these impacts could arguably be considered as extra to those identified here. Recent empirical work suggests that accounting for these channels would probably raise estimates of these committed damages, with larger damages continuing to arise in the global south 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 .

Moreover, our main empirical analysis does not explicitly evaluate the potential for impacts in local regions to produce effects that ‘spill over’ into other regions. Such effects may further mitigate or amplify the impacts we estimate, for example, if companies relocate production from one affected region to another or if impacts propagate along supply chains. The current literature indicates that trade plays a substantial role in propagating spillover effects 43 , 44 , making their assessment at the sub-national level challenging without available data on sub-national trade dependencies. Studies accounting for only spatially adjacent neighbours indicate that negative impacts in one region induce further negative impacts in neighbouring regions 45 , 46 , 47 , 48 , suggesting that our projected damages are probably conservative by excluding these effects. In Supplementary Fig. 14 , we assess spillovers from neighbouring regions using a spatial-lag model. For simplicity, this analysis excludes temporal lags, focusing only on contemporaneous effects. The results show that accounting for spatial spillovers can amplify the overall magnitude, and also the heterogeneity, of impacts. Consistent with previous literature, this indicates that the overall magnitude (Fig. 1 ) and heterogeneity (Fig. 3 ) of damages that we project in our main specification may be conservative without explicitly accounting for spillovers. We note that further analysis that addresses both spatially and trade-connected spillovers, while also accounting for delayed impacts using temporal lags, would be necessary to adequately address this question fully. These approaches offer fruitful avenues for further research but are beyond the scope of this manuscript, which primarily aims to explore the impacts of different climate conditions and their persistence.

Policy implications

We find that the economic damages resulting from climate change until 2049 are those to which the world economy is already committed and that these greatly outweigh the costs required to mitigate emissions in line with the 2 °C target of the Paris Climate Agreement (Fig. 1 ). This assessment is complementary to formal analyses of the net costs and benefits associated with moving from one emission path to another, which typically find that net benefits of mitigation only emerge in the second half of the century 5 . Our simple comparison of the magnitude of damages and mitigation costs makes clear that this is primarily because damages are indistinguishable across emissions scenarios—that is, committed—until mid-century (Fig. 1 ) and that they are actually already much larger than mitigation costs. For simplicity, and owing to the availability of data, we compare damages to mitigation costs at the global level. Regional estimates of mitigation costs may shed further light on the national incentives for mitigation to which our results already hint, of relevance for international climate policy. Although these damages are committed from a mitigation perspective, adaptation may provide an opportunity to reduce them. Moreover, the strong divergence of damages after mid-century reemphasizes the clear benefits of mitigation from a purely economic perspective, as highlighted in previous studies 1 , 4 , 6 , 24 .

Historical climate data

Historical daily 2-m temperature and precipitation totals (in mm) are obtained for the period 1979–2019 from the W5E5 database. The W5E5 dataset comes from ERA-5, a state-of-the-art reanalysis of historical observations, but has been bias-adjusted by applying version 2.0 of the WATCH Forcing Data to ERA-5 reanalysis data and precipitation data from version 2.3 of the Global Precipitation Climatology Project to better reflect ground-based measurements 49 , 50 , 51 . We obtain these data on a 0.5° × 0.5° grid from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) database. Notably, these historical data have been used to bias-adjust future climate projections from CMIP-6 (see the following section), ensuring consistency between the distribution of historical daily weather on which our empirical models were estimated and the climate projections used to estimate future damages. These data are publicly available from the ISIMIP database. See refs.  7 , 8 for robustness tests of the empirical models to the choice of climate data reanalysis products.

Future climate data

Daily 2-m temperature and precipitation totals (in mm) are taken from 21 climate models participating in CMIP-6 under a high (RCP8.5) and a low (RCP2.6) greenhouse gas emission scenario from 2015 to 2100. The data have been bias-adjusted and statistically downscaled to a common half-degree grid to reflect the historical distribution of daily temperature and precipitation of the W5E5 dataset using the trend-preserving method developed by the ISIMIP 50 , 52 . As such, the climate model data reproduce observed climatological patterns exceptionally well (Supplementary Table 5 ). Gridded data are publicly available from the ISIMIP database.

Historical economic data

Historical economic data come from the DOSE database of sub-national economic output 53 . We use a recent revision to the DOSE dataset that provides data across 83 countries, 1,660 sub-national regions with varying temporal coverage from 1960 to 2019. Sub-national units constitute the first administrative division below national, for example, states for the USA and provinces for China. Data come from measures of gross regional product per capita (GRPpc) or income per capita in local currencies, reflecting the values reported in national statistical agencies, yearbooks and, in some cases, academic literature. We follow previous literature 3 , 7 , 8 , 54 and assess real sub-national output per capita by first converting values from local currencies to US dollars to account for diverging national inflationary tendencies and then account for US inflation using a US deflator. Alternatively, one might first account for national inflation and then convert between currencies. Supplementary Fig. 12 demonstrates that our conclusions are consistent when accounting for price changes in the reversed order, although the magnitude of estimated damages varies. See the documentation of the DOSE dataset for further discussion of these choices. Conversions between currencies are conducted using exchange rates from the FRED database of the Federal Reserve Bank of St. Louis 55 and the national deflators from the World Bank 56 .

Future socio-economic data

Baseline gridded gross domestic product (GDP) and population data for the period 2015–2100 are taken from the middle-of-the-road scenario SSP2 (ref.  15 ). Population data have been downscaled to a half-degree grid by the ISIMIP following the methodologies of refs.  57 , 58 , which we then aggregate to the sub-national level of our economic data using the spatial aggregation procedure described below. Because current methodologies for downscaling the GDP of the SSPs use downscaled population to do so, per-capita estimates of GDP with a realistic distribution at the sub-national level are not readily available for the SSPs. We therefore use national-level GDP per capita (GDPpc) projections for all sub-national regions of a given country, assuming homogeneity within countries in terms of baseline GDPpc. Here we use projections that have been updated to account for the impact of the COVID-19 pandemic on the trajectory of future income, while remaining consistent with the long-term development of the SSPs 59 . The choice of baseline SSP alters the magnitude of projected climate damages in monetary terms, but when assessed in terms of percentage change from the baseline, the choice of socio-economic scenario is inconsequential. Gridded SSP population data and national-level GDPpc data are publicly available from the ISIMIP database. Sub-national estimates as used in this study are available in the code and data replication files.

Climate variables

Following recent literature 3 , 7 , 8 , we calculate an array of climate variables for which substantial impacts on macroeconomic output have been identified empirically, supported by further evidence at the micro level for plausible underlying mechanisms. See refs.  7 , 8 for an extensive motivation for the use of these particular climate variables and for detailed empirical tests on the nature and robustness of their effects on economic output. To summarize, these studies have found evidence for independent impacts on economic growth rates from annual average temperature, daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall. Assessments of daily temperature variability were motivated by evidence of impacts on agricultural output and human health, as well as macroeconomic literature on the impacts of volatility on growth when manifest in different dimensions, such as government spending, exchange rates and even output itself 7 . Assessments of precipitation impacts were motivated by evidence of impacts on agricultural productivity, metropolitan labour outcomes and conflict, as well as damages caused by flash flooding 8 . See Extended Data Table 1 for detailed references to empirical studies of these physical mechanisms. Marked impacts of daily temperature variability, total annual precipitation, the number of wet days and extreme daily rainfall on macroeconomic output were identified robustly across different climate datasets, spatial aggregation schemes, specifications of regional time trends and error-clustering approaches. They were also found to be robust to the consideration of temperature extremes 7 , 8 . Furthermore, these climate variables were identified as having independent effects on economic output 7 , 8 , which we further explain here using Monte Carlo simulations to demonstrate the robustness of the results to concerns of imperfect multicollinearity between climate variables (Supplementary Methods Section  2 ), as well as by using information criteria (Supplementary Table 1 ) to demonstrate that including several lagged climate variables provides a preferable trade-off between optimally describing the data and limiting the possibility of overfitting.

We calculate these variables from the distribution of daily, d , temperature, T x , d , and precipitation, P x , d , at the grid-cell, x , level for both the historical and future climate data. As well as annual mean temperature, \({\bar{T}}_{x,y}\) , and annual total precipitation, P x , y , we calculate annual, y , measures of daily temperature variability, \({\widetilde{T}}_{x,y}\) :

the number of wet days, Pwd x , y :

and extreme daily rainfall:

in which T x , d , m , y is the grid-cell-specific daily temperature in month m and year y , \({\bar{T}}_{x,m,{y}}\) is the year and grid-cell-specific monthly, m , mean temperature, D m and D y the number of days in a given month m or year y , respectively, H the Heaviside step function, 1 mm the threshold used to define wet days and P 99.9 x is the 99.9th percentile of historical (1979–2019) daily precipitation at the grid-cell level. Units of the climate measures are degrees Celsius for annual mean temperature and daily temperature variability, millimetres for total annual precipitation and extreme daily precipitation, and simply the number of days for the annual number of wet days.

We also calculated weighted standard deviations of monthly rainfall totals as also used in ref.  8 but do not include them in our projections as we find that, when accounting for delayed effects, their effect becomes statistically indistinct and is better captured by changes in total annual rainfall.

Spatial aggregation

We aggregate grid-cell-level historical and future climate measures, as well as grid-cell-level future GDPpc and population, to the level of the first administrative unit below national level of the GADM database, using an area-weighting algorithm that estimates the portion of each grid cell falling within an administrative boundary. We use this as our baseline specification following previous findings that the effect of area or population weighting at the sub-national level is negligible 7 , 8 .

Empirical model specification: fixed-effects distributed lag models

Following a wide range of climate econometric literature 16 , 60 , we use panel regression models with a selection of fixed effects and time trends to isolate plausibly exogenous variation with which to maximize confidence in a causal interpretation of the effects of climate on economic growth rates. The use of region fixed effects, μ r , accounts for unobserved time-invariant differences between regions, such as prevailing climatic norms and growth rates owing to historical and geopolitical factors. The use of yearly fixed effects, η y , accounts for regionally invariant annual shocks to the global climate or economy such as the El Niño–Southern Oscillation or global recessions. In our baseline specification, we also include region-specific linear time trends, k r y , to exclude the possibility of spurious correlations resulting from common slow-moving trends in climate and growth.

The persistence of climate impacts on economic growth rates is a key determinant of the long-term magnitude of damages. Methods for inferring the extent of persistence in impacts on growth rates have typically used lagged climate variables to evaluate the presence of delayed effects or catch-up dynamics 2 , 18 . For example, consider starting from a model in which a climate condition, C r , y , (for example, annual mean temperature) affects the growth rate, Δlgrp r , y (the first difference of the logarithm of gross regional product) of region r in year y :

which we refer to as a ‘pure growth effects’ model in the main text. Typically, further lags are included,

and the cumulative effect of all lagged terms is evaluated to assess the extent to which climate impacts on growth rates persist. Following ref.  18 , in the case that,

the implication is that impacts on the growth rate persist up to NL years after the initial shock (possibly to a weaker or a stronger extent), whereas if

then the initial impact on the growth rate is recovered after NL years and the effect is only one on the level of output. However, we note that such approaches are limited by the fact that, when including an insufficient number of lags to detect a recovery of the growth rates, one may find equation ( 6 ) to be satisfied and incorrectly assume that a change in climatic conditions affects the growth rate indefinitely. In practice, given a limited record of historical data, including too few lags to confidently conclude in an infinitely persistent impact on the growth rate is likely, particularly over the long timescales over which future climate damages are often projected 2 , 24 . To avoid this issue, we instead begin our analysis with a model for which the level of output, lgrp r , y , depends on the level of a climate variable, C r , y :

Given the non-stationarity of the level of output, we follow the literature 19 and estimate such an equation in first-differenced form as,

which we refer to as a model of ‘pure level effects’ in the main text. This model constitutes a baseline specification in which a permanent change in the climate variable produces an instantaneous impact on the growth rate and a permanent effect only on the level of output. By including lagged variables in this specification,

we are able to test whether the impacts on the growth rate persist any further than instantaneously by evaluating whether α L  > 0 are statistically significantly different from zero. Even though this framework is also limited by the possibility of including too few lags, the choice of a baseline model specification in which impacts on the growth rate do not persist means that, in the case of including too few lags, the framework reverts to the baseline specification of level effects. As such, this framework is conservative with respect to the persistence of impacts and the magnitude of future damages. It naturally avoids assumptions of infinite persistence and we are able to interpret any persistence that we identify with equation ( 9 ) as a lower bound on the extent of climate impact persistence on growth rates. See the main text for further discussion of this specification choice, in particular about its conservative nature compared with previous literature estimates, such as refs.  2 , 18 .

We allow the response to climatic changes to vary across regions, using interactions of the climate variables with historical average (1979–2019) climatic conditions reflecting heterogenous effects identified in previous work 7 , 8 . Following this previous work, the moderating variables of these interaction terms constitute the historical average of either the variable itself or of the seasonal temperature difference, \({\hat{T}}_{r}\) , or annual mean temperature, \({\bar{T}}_{r}\) , in the case of daily temperature variability 7 and extreme daily rainfall, respectively 8 .

The resulting regression equation with N and M lagged variables, respectively, reads:

in which Δlgrp r , y is the annual, regional GRPpc growth rate, measured as the first difference of the logarithm of real GRPpc, following previous work 2 , 3 , 7 , 8 , 18 , 19 . Fixed-effects regressions were run using the fixest package in R (ref.  61 ).

Estimates of the coefficients of interest α i , L are shown in Extended Data Fig. 1 for N  =  M  = 10 lags and for our preferred choice of the number of lags in Supplementary Figs. 1 – 3 . In Extended Data Fig. 1 , errors are shown clustered at the regional level, but for the construction of damage projections, we block-bootstrap the regressions by region 1,000 times to provide a range of parameter estimates with which to sample the projection uncertainty (following refs.  2 , 31 ).

Spatial-lag model

In Supplementary Fig. 14 , we present the results from a spatial-lag model that explores the potential for climate impacts to ‘spill over’ into spatially neighbouring regions. We measure the distance between centroids of each pair of sub-national regions and construct spatial lags that take the average of the first-differenced climate variables and their interaction terms over neighbouring regions that are at distances of 0–500, 500–1,000, 1,000–1,500 and 1,500–2000 km (spatial lags, ‘SL’, 1 to 4). For simplicity, we then assess a spatial-lag model without temporal lags to assess spatial spillovers of contemporaneous climate impacts. This model takes the form:

in which SL indicates the spatial lag of each climate variable and interaction term. In Supplementary Fig. 14 , we plot the cumulative marginal effect of each climate variable at different baseline climate conditions by summing the coefficients for each climate variable and interaction term, for example, for average temperature impacts as:

These cumulative marginal effects can be regarded as the overall spatially dependent impact to an individual region given a one-unit shock to a climate variable in that region and all neighbouring regions at a given value of the moderating variable of the interaction term.

Constructing projections of economic damage from future climate change

We construct projections of future climate damages by applying the coefficients estimated in equation ( 10 ) and shown in Supplementary Tables 2 – 4 (when including only lags with statistically significant effects in specifications that limit overfitting; see Supplementary Methods Section  1 ) to projections of future climate change from the CMIP-6 models. Year-on-year changes in each primary climate variable of interest are calculated to reflect the year-to-year variations used in the empirical models. 30-year moving averages of the moderating variables of the interaction terms are calculated to reflect the long-term average of climatic conditions that were used for the moderating variables in the empirical models. By using moving averages in the projections, we account for the changing vulnerability to climate shocks based on the evolving long-term conditions (Supplementary Figs. 10 and 11 show that the results are robust to the precise choice of the window of this moving average). Although these climate variables are not differenced, the fact that the bias-adjusted climate models reproduce observed climatological patterns across regions for these moderating variables very accurately (Supplementary Table 6 ) with limited spread across models (<3%) precludes the possibility that any considerable bias or uncertainty is introduced by this methodological choice. However, we impose caps on these moderating variables at the 95th percentile at which they were observed in the historical data to prevent extrapolation of the marginal effects outside the range in which the regressions were estimated. This is a conservative choice that limits the magnitude of our damage projections.

Time series of primary climate variables and moderating climate variables are then combined with estimates of the empirical model parameters to evaluate the regression coefficients in equation ( 10 ), producing a time series of annual GRPpc growth-rate reductions for a given emission scenario, climate model and set of empirical model parameters. The resulting time series of growth-rate impacts reflects those occurring owing to future climate change. By contrast, a future scenario with no climate change would be one in which climate variables do not change (other than with random year-to-year fluctuations) and hence the time-averaged evaluation of equation ( 10 ) would be zero. Our approach therefore implicitly compares the future climate-change scenario to this no-climate-change baseline scenario.

The time series of growth-rate impacts owing to future climate change in region r and year y , δ r , y , are then added to the future baseline growth rates, π r , y (in log-diff form), obtained from the SSP2 scenario to yield trajectories of damaged GRPpc growth rates, ρ r , y . These trajectories are aggregated over time to estimate the future trajectory of GRPpc with future climate impacts:

in which GRPpc r , y =2020 is the initial log level of GRPpc. We begin damage estimates in 2020 to reflect the damages occurring since the end of the period for which we estimate the empirical models (1979–2019) and to match the timing of mitigation-cost estimates from most IAMs (see below).

For each emission scenario, this procedure is repeated 1,000 times while randomly sampling from the selection of climate models, the selection of empirical models with different numbers of lags (shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) and bootstrapped estimates of the regression parameters. The result is an ensemble of future GRPpc trajectories that reflect uncertainty from both physical climate change and the structural and sampling uncertainty of the empirical models.

Estimates of mitigation costs

We obtain IPCC estimates of the aggregate costs of emission mitigation from the AR6 Scenario Explorer and Database hosted by IIASA 23 . Specifically, we search the AR6 Scenarios Database World v1.1 for IAMs that provided estimates of global GDP and population under both a SSP2 baseline and a SSP2-RCP2.6 scenario to maintain consistency with the socio-economic and emission scenarios of the climate damage projections. We find five IAMs that provide data for these scenarios, namely, MESSAGE-GLOBIOM 1.0, REMIND-MAgPIE 1.5, AIM/GCE 2.0, GCAM 4.2 and WITCH-GLOBIOM 3.1. Of these five IAMs, we use the results only from the first three that passed the IPCC vetting procedure for reproducing historical emission and climate trajectories. We then estimate global mitigation costs as the percentage difference in global per capita GDP between the SSP2 baseline and the SSP2-RCP2.6 emission scenario. In the case of one of these IAMs, estimates of mitigation costs begin in 2020, whereas in the case of two others, mitigation costs begin in 2010. The mitigation cost estimates before 2020 in these two IAMs are mostly negligible, and our choice to begin comparison with damage estimates in 2020 is conservative with respect to the relative weight of climate damages compared with mitigation costs for these two IAMs.

Data availability

Data on economic production and ERA-5 climate data are publicly available at https://doi.org/10.5281/zenodo.4681306 (ref. 62 ) and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 , respectively. Data on mitigation costs are publicly available at https://data.ene.iiasa.ac.at/ar6/#/downloads . Processed climate and economic data, as well as all other necessary data for reproduction of the results, are available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

Code availability

All code necessary for reproduction of the results is available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

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Acknowledgements

We gratefully acknowledge financing from the Volkswagen Foundation and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the Government of the Federal Republic of Germany and Federal Ministry for Economic Cooperation and Development (BMZ).

Open access funding provided by Potsdam-Institut für Klimafolgenforschung (PIK) e.V.

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All authors contributed to the design of the analysis. M.K. conducted the analysis and produced the figures. All authors contributed to the interpretation and presentation of the results. M.K. and L.W. wrote the manuscript.

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Extended data figures and tables

Extended data fig. 1 constraining the persistence of historical climate impacts on economic growth rates..

The results of a panel-based fixed-effects distributed lag model for the effects of annual mean temperature ( a ), daily temperature variability ( b ), total annual precipitation ( c ), the number of wet days ( d ) and extreme daily precipitation ( e ) on sub-national economic growth rates. Point estimates show the effects of a 1 °C or one standard deviation increase (for temperature and precipitation variables, respectively) at the lower quartile, median and upper quartile of the relevant moderating variable (green, orange and purple, respectively) at different lagged periods after the initial shock (note that these are not cumulative effects). Climate variables are used in their first-differenced form (see main text for discussion) and the moderating climate variables are the annual mean temperature, seasonal temperature difference, total annual precipitation, number of wet days and annual mean temperature, respectively, in panels a – e (see Methods for further discussion). Error bars show the 95% confidence intervals having clustered standard errors by region. The within-region R 2 , Bayesian and Akaike information criteria for the model are shown at the top of the figure. This figure shows results with ten lags for each variable to demonstrate the observed levels of persistence, but our preferred specifications remove later lags based on the statistical significance of terms shown above and the information criteria shown in Extended Data Fig. 2 . The resulting models without later lags are shown in Supplementary Figs. 1 – 3 .

Extended Data Fig. 2 Incremental lag-selection procedure using information criteria and within-region R 2 .

Starting from a panel-based fixed-effects distributed lag model estimating the effects of climate on economic growth using the real historical data (as in equation ( 4 )) with ten lags for all climate variables (as shown in Extended Data Fig. 1 ), lags are incrementally removed for one climate variable at a time. The resulting Bayesian and Akaike information criteria are shown in a – e and f – j , respectively, and the within-region R 2 and number of observations in k – o and p – t , respectively. Different rows show the results when removing lags from different climate variables, ordered from top to bottom as annual mean temperature, daily temperature variability, total annual precipitation, the number of wet days and extreme annual precipitation. Information criteria show minima at approximately four lags for precipitation variables and ten to eight for temperature variables, indicating that including these numbers of lags does not lead to overfitting. See Supplementary Table 1 for an assessment using information criteria to determine whether including further climate variables causes overfitting.

Extended Data Fig. 3 Damages in our preferred specification that provides a robust lower bound on the persistence of climate impacts on economic growth versus damages in specifications of pure growth or pure level effects.

Estimates of future damages as shown in Fig. 1 but under the emission scenario RCP8.5 for three separate empirical specifications: in orange our preferred specification, which provides an empirical lower bound on the persistence of climate impacts on economic growth rates while avoiding assumptions of infinite persistence (see main text for further discussion); in purple a specification of ‘pure growth effects’ in which the first difference of climate variables is not taken and no lagged climate variables are included (the baseline specification of ref.  2 ); and in pink a specification of ‘pure level effects’ in which the first difference of climate variables is taken but no lagged terms are included.

Extended Data Fig. 4 Climate changes in different variables as a function of historical interannual variability.

Changes in each climate variable of interest from 1979–2019 to 2035–2065 under the high-emission scenario SSP5-RCP8.5, expressed as a percentage of the historical variability of each measure. Historical variability is estimated as the standard deviation of each detrended climate variable over the period 1979–2019 during which the empirical models were identified (detrending is appropriate because of the inclusion of region-specific linear time trends in the empirical models). See Supplementary Fig. 13 for changes expressed in standard units. Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Extended Data Fig. 5 Contribution of different climate variables to overall committed damages.

a , Climate damages in 2049 when using empirical models that account for all climate variables, changes in annual mean temperature only or changes in both annual mean temperature and one other climate variable (daily temperature variability, total annual precipitation, the number of wet days and extreme daily precipitation, respectively). b , The cumulative marginal effects of an increase in annual mean temperature of 1 °C, at different baseline temperatures, estimated from empirical models including all climate variables or annual mean temperature only. Estimates and uncertainty bars represent the median and 95% confidence intervals obtained from 1,000 block-bootstrap resamples from each of three different empirical models using eight, nine or ten lags of temperature terms.

Extended Data Fig. 6 The difference in committed damages between the upper and lower quartiles of countries when ranked by GDP and cumulative historical emissions.

Quartiles are defined using a population weighting, as are the average committed damages across each quartile group. The violin plots indicate the distribution of differences between quartiles across the two extreme emission scenarios (RCP2.6 and RCP8.5) and the uncertainty sampling procedure outlined in Methods , which accounts for uncertainty arising from the choice of lags in the empirical models, uncertainty in the empirical model parameter estimates, as well as the climate model projections. Bars indicate the median, as well as the 10th and 90th percentiles and upper and lower sixths of the distribution reflecting the very likely and likely ranges following the likelihood classification adopted by the IPCC.

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Kotz, M., Levermann, A. & Wenz, L. The economic commitment of climate change. Nature 628 , 551–557 (2024). https://doi.org/10.1038/s41586-024-07219-0

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Note that the Economics 103 Case Studies are meant to supplement the course material by giving you experience applying Economic concepts to real world examples. While they are beyond the level you will be tested on, they are useful for students who want a stronger grasp of the concepts and their applications.

Note that this case study is difficult if you do not print the diagrams, or reproduce them on graph paper. If you are unable to print, we recommend reviewing the solutions to ensure you understand the general lessons presented.

In 2016 rental vacancy rates dropped to as low as 0.6% in Greater Victoria. When compared to the national average of 3.3% it is clear why many media channels and individuals were calling it a ‘housing crisis’. Students were especially hit hard by these low vacancies, with some international students at Camoson college having to return home when they couldn’t find a place to stay. Using our competitive market model, let’s examine some of the factors that played into this crisis and policies that could be used to fix it.

Read more about the Victoria Housing crisis.

Below is a representation of the Victoria Housing Market.

1. Label Figure CS3 a. with the Equilibrium price and quantity, and label supply and demand curves as either renters or landlords.

If supply is equal to demand there should be no vacancy, but we know that a 0% vacancy rate would be an extremely difficult market for renters.

2. Explain why a housing market at equilibrium could still have a vacancy rate of 4%.

One factor that has been blamed for the housing crisis is Airbnb. Airbnb describes itself as  an online marketplace and hospitality service, enabling people to list or rent short-term lodging including vacation rentals, apartment rentals, homestays, hostel beds, or hotel rooms, with the cost of lodging set by the property owner. City councillors have targeted these short term rentals, saying that many landlords have opted to Airbnb their home, rather than rent out longer term. The growth of Airbnb in Vancouver has been shown below.

3. Assume 3000 landlords decide to switch from renting to Airbnb, show the impact of the changes on Figure CS3 b. Label the new equilibrium price and quantity.

Note that Airbnb has been adamant that short-term rentals have had a neglible impact on the housing market, citing that in Vancouver only 320 hosts rent out thier properties often enough to make more money that long term rentals. That represents only 0.11% of the total housing units. In Victoria, that would mean only 25 units are affected by short-term rentals.

Tom Davidoff , a  University of B.C. business professor, said t he general public frequently looks at the fact that Airbnb is popular in expensive neighbourhoods and concludes that it is Airbnb that drives up rents there. But, he said, those neighbourhoods were expensive anyway and the impact of Airbnb taking a certain slice of the available stock is minimal.

Read more about Airbnb’s supposed impact on the market.

Another factor that has had an impact on the rental market is the inability of many young Canadians to buy homes. Not only have house prices skyrocketed, but more are burdened by student loans out of university. It is estimated that it takes 3 times longer (15 years) to save enough to have a 20% downpayment on a house than it did in 1976. This reduction in Canadians mortgaging a home has caused an increasing amount to enter the market.

Read more about the increasing squeeze on millennials.

4. Assume 9000 new renters enter the market instead of mortgaging homes, show the impact of the changes on Figure CS3 b.  Explain the impact of both the shock from Airbnb and the shock from less housing buyers on equilibrium price and quantity. Do the shocks work together or oppose one another?

In the housing market, prices are slow to adjust, landlords cannot simply raise prices immediately under the Residential Tenancy Act. Landlords can only raise prices when negotiating a new contract. This causes many unjustified evictions from landlords as they want to charge the new equilibrium price. In the short-term, prices stay relatively the same causing shortages or surplus.

5. Assume price remains at the original equilibrium , calculate the magnitude of the shortage or surplus of housing that results. Explain the impact this shortage will have the behaviour of landlords.

The British Columbia government unveiled a $500-million affordable-housing plan targeted at communities that have struggled with a shortage of low-cost housing.  Premier Christy Clark announced her government’s commitment to fund 68 new projects to help address the crisis

Read more about the governments response to the housing crisis.

6. Assume the government wants to bring price back to it’s original level, if it costs $50,000 to increase the number of rental units by one, how much will this cost the government?

Groups have criticized the government response, saying that they have ignored many other avenues that could more easily increase the supply of affordable housing.

7. Read the Executive Summary of the Alliance of BC Students White Paper on Student Housing.

What is the ABCS proposing that could help decrease price in the market? How would this affect supply and/or demand?

In this case study we have shown how microeconomic concepts of supply and demand can be used to understand current events in the news. Do you have a story you think would make a good case study? Contact [email protected] to propose your story.

Principles of Microeconomics Copyright © 2017 by University of Victoria is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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Case Studies for Teaching Economics

This is an index of case studies in economics. Teaching case studies are on another part of the site.

Introductory

• Principles (General) (1)
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Solving Case Study in Economics: A Complete Guide

case study in economics

Case studies are invaluable in economics education, providing students with real-world scenarios to apply theoretical concepts and analytical skills. However, solving a case study in economics requires a structured approach that combines research, critical thinking, and a deep understanding of economic principles. This post presents a comprehensive guide on effectively solving a case study in economics , ensuring a thorough analysis and a grasp of practical implications.

Understanding the Case Study

Read carefully: .

Begin by reading the case study thoroughly. Pay attention to the details, context, and objectives presented. Identify the main issues, stakeholders, and the economic concepts at play.

Define the Problem: 

Clearly define the economic problem or challenge presented in the case study. What are the fundamental problems that ought to be handled? Understanding the problem is crucial before proceeding with analysis.Identify the central economic problem or challenge that the case study presents. This could involve issues related to demand and supply, market structures, externalities, government interventions, or any other economic concept.

Gathering Relevant Information

Research: .

Conduct thorough research to gather additional information relevant to the case study. This may involve exploring economic theories, statistical data, and industry trends. Reliable sources such as academic journals, government reports, and reputable news outlets are valuable.

Identify Variables: 

Identify the variables affecting the situation presented in the case study. These could include economic indicators, market conditions, government policies, etc.

Read also: How Trademark Registration Can Help in Business

Applying Economic Concepts

Use relevant theories: .

Apply relevant economic theories and concepts to analyze the case study. Consider concepts like supply and demand, elasticity, market structure, cost analysis, and utility theory, depending on the case context.

Quantitative Analysis:  

If applicable, use quantitative methods such as calculations, graphs, and charts to illustrate your analysis. These tools can help visualize economic relationships and trends.

Data Interpretation

Cause and effect:.

 Identify the cause-and-effect relationships driving the economic situation in the case study. Analyze how changes in one variable can impact others and lead to specific outcomes.

Consider Alternatives: 

Explore solutions or strategies to address the issues presented. Consider the possible benefits and drawbacks of each option.

Making Recommendations

Informed decisions: .

Based on your analysis, make informed recommendations for addressing the challenges outlined in the case study in economics. Your recommendations should be rooted in economic theories and supported by your gathered data.

Justify Your Recommendations: 

Clearly explain the rationale behind your recommendations. How will they positively impact the stakeholders involved? Justify your choices with economic logic.

Read also: Best Practices for Workday Financial Management Integration

Tips for Success

Practice: .

The more case studies you solve, the more comfortable you’ll become with the process. Practice hones your analytical skills and enables you to apply economic concepts effectively.

Collaborate: 

Engage in discussions with peers or instructors. Collaborative analysis can offer diverse perspectives and deepen your understanding of the case.

Real-World Context: 

Relate the case study to real-world economic scenarios. Understanding the practical implications of your analysis adds depth to your recommendations.

Stay Updated:

 Case study in economics is a dynamic field. Stay updated with current economic trends, policy changes, and market developments to enhance the relevance of your analysis.

Read the Case Thoroughly

Begin by reading the case study attentively. Familiarize yourself with the context, characters, and economic issues presented. Take notes as you read to highlight key information and identify the main problems.

 Apply Relevant Economic Concepts

Next, apply the economic concepts and theories you’ve learned in your coursework to the identified problem. Consider how concepts like elasticity, opportunity cost, marginal analysis, and cost-benefit analysis can be applied to the situation.

 Collect Data and Information

Gather relevant data and information that can support your analysis. This may include statistical data, market trends, historical information, and other relevant sources that substantiate your arguments.

Analyze and Evaluate

Conduct a thorough analysis of the situation. Identify the factors contributing to the problem and evaluate their impact. Use graphs, charts, and diagrams to represent your analysis and provide clarity visually.

 Explore Alternatives

Generate possible solutions or alternatives to address the identified problem. Consider the pros and cons of each solution, keeping in mind economic feasibility, ethical implications, and potential outcomes.

 Apply Economic Theories

When formulating solutions, apply economic theories and principles that align with the situation. For instance, if you’re dealing with a market failure, explore how government intervention or corrective measures can be applied based on economic theories like externalities or public goods.

Quantitative Analysis

If applicable, perform quantitative analysis using relevant mathematical or statistical tools. This could involve calculating elasticity break-even points or analyzing cost structures to support your recommendations.

 Justify Your Recommendations

Ensure that your solutions are well-justified and backed by solid economic reasoning. Explain how each solution addresses the problem and aligns with economic theories.

 Consider Real-World Constraints

Acknowledge any real-world constraints that might affect the implementation of your recommendations. This could include budgetary limitations, political considerations, or social factors.

Solving an case study in economics writing is an enriching experience that bridges theory and practice. It requires a structured approach, from understanding the case to making well-informed recommendations. By thoroughly analyzing the economic concepts, interpreting data, and applying relevant theories, you can arrive at strategic solutions that align with economic principles.

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Topic 7: Producer Theory

Some hate it and some love it, but regardless of how you feel oil is still a key part of our daily lives. The average Canadian uses about 20 barrels of oil each year, equivalent to about one and a half swimming pools. Since it is such a major part of our expenses, oil is a product where, when price changes, we really notice.

In 2008, China’s expansion sparked  a  long period of high prices. In this case study, we will analyze what has happened to these prices over time and the impact this has had on oil producers from the lens of producer theory. 

To simplify our case study, let’s assume that the oil market is perfect competition.

1. Consider the following producer theory model for a single firm producing oil, and the aggregate supply and demand. What is the firm’s equilibrium price and quantity?

2. What is the firm’s profit at this level?

3. What will occur in the long-run for this market? Show this on the new graph below.

The period of high prices indeed incentivized private firms to search farther than ever before –  in the Arctic, Brazil’s pre-salt fields, deep waters off Angola, and Canada’s oil sands to ever expand the supply of oil. Investors encouraged this activity, rewarding future growth as much as profitability.

Oil prices have been quite volatile. Recently, from mid 2014 to early 2016 oil prices plummeted from $110 a barrel to around $27. This sharp decline has been due to a number of causes. A key cause was when sanctions were lifted from Iran, a new producer entered the market with large quantities of oil. In addition, growing fears about action on climate change, coupled with the emergence of alternative-energy technologies, caused producers to pump as hard as they can, while they can.

Read more about the reasons for changing oil prices

3. Use the producer theory model from above to show the impact of Iran’s entry, assume this brings the market from long-run equilibrium to a price of $27.

4. What are the firms profits at this price?

In the industry at large, the incentive is to keep producing “as flat out as you can”, once investment costs have been sunk into the ground, says Simon Henry, Shell’s chief financial officer. He says it is sometimes more expensive to stop production than to keep pumping at low prices, because of the high cost of mothballing wells. He suggested that firms  will not pack up so long as prices cover day-to-day costs, in some cases as low as $15 a barrel.

It may be uneconomic to drill new deepwater wells at prices under $60 a barrel, he says, but once they are built it may still make economic sense to keep them running at prices well below that. Such resilience is used by some to justify why they expect prices to remain “lower for longer”.

Read more about the impact of price changes on oil firms

6. What does this excerpt suggest about how firms will behave in the short run?

7. Based on the information given about this market, what do you think the time horizon will be for this industries ‘long run’? What will happen in the long run?

Notice that in our model when prices fall, even in the short run individual firms will decrease production. In reality, firms part of the Organization of Petroleum Exporting Countries (OPEC) made a pact to keep production high, to try to retain market share and keep out competitors.

In his book “The Prize”, Daniel Yergin quotes an American academic writing as far back as 1926 about the “spectacle” of massive overproduction. “All saw the remedy but would not adopt it. The remedy was, of course, a reduction in the production.”

8. Comment on the effects of OPEC’s actions on the market.

In this case study we have shown how microeconomic concepts of monopoly and monopolistic competition can be used to understand current events in the news. Do you have a story you think would make a good case study? Contact [email protected] to propose your own case.

Principles of Microeconomics Copyright © 2017 by University of Victoria is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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Top 40 Most Popular Case Studies of 2021

Two cases about Hertz claimed top spots in 2021's Top 40 Most Popular Case Studies

Two cases on the uses of debt and equity at Hertz claimed top spots in the CRDT’s (Case Research and Development Team) 2021 top 40 review of cases.

Hertz (A) took the top spot. The case details the financial structure of the rental car company through the end of 2019. Hertz (B), which ranked third in CRDT’s list, describes the company’s struggles during the early part of the COVID pandemic and its eventual need to enter Chapter 11 bankruptcy. 

The success of the Hertz cases was unprecedented for the top 40 list. Usually, cases take a number of years to gain popularity, but the Hertz cases claimed top spots in their first year of release. Hertz (A) also became the first ‘cooked’ case to top the annual review, as all of the other winners had been web-based ‘raw’ cases.

Besides introducing students to the complicated financing required to maintain an enormous fleet of cars, the Hertz cases also expanded the diversity of case protagonists. Kathyrn Marinello was the CEO of Hertz during this period and the CFO, Jamere Jackson is black.

Sandwiched between the two Hertz cases, Coffee 2016, a perennial best seller, finished second. “Glory, Glory, Man United!” a case about an English football team’s IPO made a surprise move to number four.  Cases on search fund boards, the future of malls,  Norway’s Sovereign Wealth fund, Prodigy Finance, the Mayo Clinic, and Cadbury rounded out the top ten.

Other year-end data for 2021 showed:

• Online “raw” case usage remained steady as compared to 2020 with over 35K users from 170 countries and all 50 U.S. states interacting with 196 cases.
• Fifty four percent of raw case users came from outside the U.S..
• The Yale School of Management (SOM) case study directory pages received over 160K page views from 177 countries with approximately a third originating in India followed by the U.S. and the Philippines.
• Twenty-six of the cases in the list are raw cases.
• A third of the cases feature a woman protagonist.
• Orders for Yale SOM case studies increased by almost 50% compared to 2020.
• The top 40 cases were supervised by 19 different Yale SOM faculty members, several supervising multiple cases.

CRDT compiled the Top 40 list by combining data from its case store, Google Analytics, and other measures of interest and adoption.

All of this year’s Top 40 cases are available for purchase from the Yale Management Media store .

And the Top 40 cases studies of 2021 are:

1.   Hertz Global Holdings (A): Uses of Debt and Equity

2.   Coffee 2016

3.   Hertz Global Holdings (B): Uses of Debt and Equity 2020

4.   Glory, Glory Man United!

5.   Search Fund Company Boards: How CEOs Can Build Boards to Help Them Thrive

6.   The Future of Malls: Was Decline Inevitable?

7.   Strategy for Norway's Pension Fund Global

8.   Prodigy Finance

9.   Design at Mayo

10. Cadbury

11. City Hospital Emergency Room

13. Volkswagen

14. Marina Bay Sands

15. Shake Shack IPO

16. Mastercard

17. Netflix

18. Ant Financial

19. AXA: Creating the New CR Metrics

20. IBM Corporate Service Corps

21. Business Leadership in South Africa's 1994 Reforms

22. Alternative Meat Industry

23. Children's Premier

24. Khalil Tawil and Umi (A)

25. Palm Oil 2016

26. Teach For All: Designing a Global Network

27. What's Next? Search Fund Entrepreneurs Reflect on Life After Exit

28. Searching for a Search Fund Structure: A Student Takes a Tour of Various Options

30. Project Sammaan

31. Commonfund ESG

32. Polaroid

33. Connecticut Green Bank 2018: After the Raid

34. FieldFresh Foods

35. The Alibaba Group

36. 360 State Street: Real Options

37. Herman Miller

38. AgBiome

39. Nathan Cummings Foundation

40. Toyota 2010

How Indigenous expertise is empowering climate action: A case study from Oceania

Indigenous Peoples are the world's foremost experts on preserving the climate and biodiversity — listening to them is the key to protecting our planet. Image:  REUTERS/Dylan Martinez

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• Industrial-scale capitalism and mass urbanization have accelerated environmental degradation, necessitating a reconnection with nature and Indigenous expertise.
• Indigenous knowledge systems offer holistic approaches to environmental sustainability and are increasingly recognized for their value in climate action.
• Examples like traditional fire management and innovative applications of Indigenous knowledge demonstrate the potential for collaboration and cross-pollination between Indigenous and other practices to address climate and ecological challenges.

On Monday 15 April 2024 , the largest global annual gathering of Indigenous Peoples kicked off at the United Nations Headquarters in New York, with participants calling for greater efforts to close financial gaps for Indigenous Peoples. Next month, Sydney Climate Action Week during May 13-19 will take place in Australia, a series of community-led events across Sydney from all aspects of the climate action ecosystem.

Such a coming together of stakeholders presents a prime opportunity to explore how traditional regional innovation models can contribute global solutions under a more inclusive climate agenda.

Have you read?

Renewables projects must respect indigenous peoples and local communities. here's how, indigenous leaders bringing their knowledge to davos 2024, 5 ways indigenous people are protecting the planet, disconnection from nature is exacerbating climate change.

Industrial-scale capitalism has accelerated the erosion of climate and ecological systems. Mass urbanization has driven economic growth and extractive industries on scales previously unimaginable. Sadly, these trends are increasing. Around 2007, the United Nations estimated , we had reached a tipping point in the world: more people were living in urban than rural areas. By 2022, 57% of all humans became urban based. In developed nations, a whopping 80% of citizens are urban. Both of these trends are predicted to increase .

This is reflected in how we are responding to the climate emergency. We are rapidly losing our connection to nature, with scarce nature feedback loops in citified life. This skews our perceptions and worldviews. Our academies are urban, our scientists are trained and our businesspeople, financiers, teachers, politicians are upskilled in mostly urban landscapes. Atrophied natural knowledge leaves us unequipped for dealing with the complexity of ecosystems. We need to humbly acknowledge the true experts in nature, Indigenous people, and ask for their guidance, meeting with our resources to activate this expertise.

Indigenous knowledge in climate action

“The planet’s ill health has largely come about because humans have forgotten their relationship and responsibility to country. Imagine if we could tap into the way First Nations cultures focus on deep, holistic connections to the environment to help us rethink environmental and health policies.”

Although Indigenous knowledge systems have been built on millennia of lived experience, iteration and adaptation, they have too often been ignored.

Today, however, there is growing interest in the value of Indigenous knowledge systems in the wake of environmental and climate challenges. In contrast to non-indigenous value systems, indigenous value systems focus on environmental sustainability as an end in itself that is required for cultural, social and economic well-being. The knowledge we have lost is still very much alive in Indigenous worlds, built through millennia of observation and ecological management.

The world needs to reframe both the role and deep knowledge of Indigenous people as experts. Signs of this shift are already emerging. In carbon and emerging biodiversity markets , any project involving, run by or partnered with Indigenous people attracts a premium carbon credit price in recognition of the deeper integrity, provenance and permanence Indigenous people bring that mitigate greenwashing claims.

Harnessing Indigenous expertise for modern global impact

An example of superior Indigenous technology is found in traditional fire management . Western fuel reduction strategies have triggered catastrophic fires when containment lines are broken, but Indigenous people have practiced for millennia “cool burning” that allows time for biodiversity to move to safety. This knowledge is needed across Europe and North America, both of which have experienced staggering brush fires. According to Firesticks , the not-for-profit Indigenous network, “aboriginal fire management has become a priority for community, cultural, social and environmental wellbeing”. This awareness showcases an example of the acknowledged inter-relatedness of knowledge, land, culture and identity — and the prioritization of environmental wellbeing alongside the community and culture.

Alongside ancient practices sit novel applications of Indigenous knowledge. Rainstick, a novel biotech company that was inspired by the traditions of the Maiawali People of central west Queensland, Australia, has built on a 10,000-year-old practice that acknowledges the influence of lightning on how plants grow. The resulting approach combines ancient expertise with modern technology to show that indigenous knowledge is ever-changing and can be applied in new and novel ways for climate action. Another example can be found in Savimbo fair trade credits , which is Indigenous at its core — removing intermediaries to ensure Indigenous experts can attract capital to resist further incursion into precious Indigenous-managed lands by developers.

Indigenous people are also sharing their profound knowledge with each other to manage and fix the problem of climate change. In the Ampliseed network, Indigenous people managing lands as diverse as the snow-covered Boreal Forest in Canada, World Heritage Listed Coral Reefs, tropical rainforest in the Peruvian Amazon, Mediterranean habitat in Chile and Australia’s 10 vast central deserts are cross-pollinating their knowledge, navigating the climate disaster and creating global impact at the Climate and Biodiversity CoPs .

Next steps for applying Indigenous expertise

Given that 80% of remaining biodiversity is on Indigenous lands and in Indigenous hands, it is clear with the emerging biodiversity markets that Indigenous people are the primary market actors. Therefore, financing models need to evolve in order to safeguard and invest in the role of Indigenous people as stewards and equity owners of the conservation of the precious remaining biodiversity. Similarly, financing all other opportunities to cross-pollinate Western and Indigenous knowledge systems is essential.

The growing body of evidence reveals a simple truth: Indigenous people hold a key to our collective response to climate and ecological challenges.

Indigenous-led organizations are thriving by sharing this knowledge, and people and the planet are benefiting in turn.

With contributions from Harry Guinness, Head of Net Zero Strategy, Greenhouse.

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The implementation of export-import (e-i) subsidies regulation on rooftop photovoltaic plant system in indonesia based on techno-economic point of view: a study case in ogan komering ulu region, south sumatera, indonesia.

© 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license ( http://creativecommons.org/licenses/by/4.0/ ).

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This study aims to analyze the techno-economic impact of implementing a variety of export-import subsidies on the rooftop photovoltaic plant based on two Ministerial Energy and Mineral Resource (MEMR) in Indonesia: MEMR 49/2018 and MEMR 26/2021. Five of the economic parameters were used to investigate the effect of two regulations with four E-I scenarios. The result showed that the photovoltaic energy in Indonesia is considered as high potential with an average photovoltaic energy of 4.8 kWh/m 2 . The technical analysis presents that the energy could supply the average society's electricity demand. On the economic analysis, the result showed that all the scenarios based on two MEMR showed a positive net present value (NPV) indicating all the implementations are profitable. However, the scenario based on the new MEMR 26/21 with 100% E-I showed the half time of NPV indicates a less risky project compared to others. On the other hand, the result proved that 100% E-I scenario could be the best scenario to stimulate the RPP development since all the economic parameters showed a massive improvement. The value of Net B/C and ROI with MEMR 26/2021 shifted to approximately 63% and 21.3%, respectively, compared to the scenario on MEMR 49/2018. This result confirmed that the new MEMR 26/2021 could stimulate the growth of rooftop photovoltaic in Indonesia for energy transition to achieve 3.6 Giga-watts Solar Photovoltaic Energy (SPE) Mix by 2025.

renewable energy, export-import subsidies, PV solar energy, Indonesia

Renewable Energy (RNE) resources have been expanded up to 29% of world energy demand in 2021 which was a growth up to 3% compared to energy demand in 2019 [1]. Our World in Data reported that Hydro-Power Energy (HPE) contributed the highest RNE source followed by Windmill Energy (MWE) and Solar Photovoltaic Energy (SPE) [2]. Moreover, the SPE showed the most improved power generation in the last 10 years where the power generation improvement was moved from 31 Terra-watts (TW) in 2010 to 1289 TW in 2022 [3]. The fast growth of SPE is because the SPE stands out as one of the most promising alternatives to increase the utilization of RNE [4]. The SPE is relatively easy to maintain and the cost of having SPE including utility, commercial, and residential keeps declining in the last decade [5]. Furthermore, the SPE is considered as the most feasible energy in terms of technical and economic perspectives which could not only supply energy to society but also support the finances of the society [6, 7].

The International Renewable Energy Agency (IRENA) has published the top 10 countries that have electricity capacity by the SPE [8]. However, Indonesia has not shown good progress in the SPE development since Indonesia only produces 0.2 GW of SPE [9]. This fact could not be a piece of good news since Indonesia has an important role in reducing greenhouse emissions. As the top fourth (4 th ) of the largest population with 279 million people [10]. the successful energy transition to RNE in Indonesia could provide a significant change to the global environment. Furthermore, Indonesia contributes approximately 36% of energy production which includes the energy coming from fossil and RNE resources in ASEAN [11]. In the case of SPE facts, Indonesia has a very high potential for SPE resources due to its advantageous geographical location. The Global Horizontal Irradiance (GHI) of Indonesia is around 4.2 kWh/m 2 to 5.0 kWh/m 2 which could produce approximately 3.4 kWh/kWp to 4.0 kWh/kWp of Solar Photovoltaic Electricity (SPE) [12].

The Government of the Republic of Indonesia (GORI) has planned several strategies to enhance RNE and SPE development. The GORI has published the National General Electricity Plan where the share of RNE was targeted to 23% of Indonesia's total energy generation mix by 2025 [13]. However, the progress is little where IRENA has published the total RNE energy mix coming from RNE only produced 10,554 Megawatts (MW) or 12.2% [14] whereas SPE source only contributed 0.078% or 1.89% from the report published by the Institute of Essential Services Reform (IESR) [9].

The target of 23% of the energy mix from RNE seems impossible to achieve in 2025 but the GORI has another plan. The GORI put a goal on the SPE development to achieve 3.6 GW of the SPE by 2025 comprising the Rooftop Photovoltaic Plant (RPP), large-scale or commercial Photovoltaic Plant (LCPP), and floating Photovoltaic Plant (FPP) [15]. The target of 3.6 GW could be possible since IESR estimates the RPP in the capital cities of Jakarta and Surabaya could contribute approximately 2 GWp of SPE Alone [16]. Considering other cities in Indonesia, the target could be reached by only applying the RPP in the city's residential markets.

To achieve the target of 3.6 GW of the SPE in RNE Mix in 2025, the GORI has implemented the Ministerial Regulation of the Minister of Energy and Mineral Resources (MEMR) No 49/2018 and which regulates the Use of Rooftop Solar Power Generation Systems by Consumers of PT. Perusahaan Listrik Negara. The regulation specifies the export-import (E-I) scenario where PT. PLN could purchase the exceed of electrical production of RPP by giving the subsidy which could be an additional income for the owner of RPP System. However, only 65% of electrical exceed could be claimed as a subsidy which seems not to optimally stimulate the growth of the SPE development. Therefore, the GORI revises the MEMR No. 49/2018 by publishing the new MEMR No. 26/2021 which could cover the electrical exceed and provide a 100% subsidy to the owner of RPS. However, MEMR No. 26/2021 has been delayed temporarily by the House of Representatives of the Republic of Indonesia to ensure the impact of implementing the MEMR on subsidies and compensation in the State Revenue and Expenditure Budget [15]. Therefore, the MEMR No 26/2021 has been implemented since the beginning of 2022. On the other hand, there is no comprehensive study that explores the effect of new regulations or compares two regulations from the techno-economic perspective. This information is important to see how much the economic advantages are offered in the new MEMR.

The present study aims to do the techno-economic analysis of the implementation of two MEMRs about the Export-Import Subsidies of RPP in Indonesia. The study is focused on answering how much economic shifts have been made by forming a new regulation related to E-I subsidies in the RPP. The study began with investigating the number of SPE potential in the studied area and how much investment is needed. It is important to ensure that the energy cost does not exceed the cost of electricity in Indonesia. Furthermore, the economic analysis will use four scenarios that have been designed to mimic the MEMR 49/2018 with 65% E-I scenarios and the MEMR 26/2021 with 100% E-I scenarios. Two additional scenarios were added between 65% and 100% E-I scenarios to provide a better understanding of economic shifts as the function of E-I scenarios. The study could provide a better understanding and initial assessment about how the growth of PV investment in Indonesia will be in the future The result could provide important information, especially for the people who want to invest in the RPP about how profitable the SPP and the risky level of investing RPP.

2.1 The simulating location

The present study uses the region of the Ogan Komering Ulu (OKU), South Sumatera, Indonesia as the simulating location. The OKU region has the geographical coordinates of 4.0283 ° S and 104.0072 ° E. The capital city of the OKU district is Baturaja and located by the primary artery road of the province of South Sumatra. The main reason to choose the OKU regency is to change the background of the OKU regency which is known as the energy barn of Indonesia since there are several coal-based mining industries located in Indonesia. By giving a comprehensive study related to green energy in the OKU regency, the OKU regency could not be known not only as the fossil-based energy barn but also as the green energy barn in Indonesia. On the other hand, most of the RPP development is focused on the capital city where the regency-based city and remote areas are not well investigated. Thus, the present study could provide a different perspective on technoeconomic analysis in the capital of regency. The result could be valuable information and has originality for the local government of the OKU region and the stakeholders to understand the potential of the SPE in the region of the OKU.

2.2 Assessing the global horizontal irradiation (GHI) and energy simulated production

Assessing the GHI began the present study where the GHI represents the potential of the SPE in the studied area. The GHI was assessed using the PVsyst software which was developed at the University of Geneva, Switzerland [6]. The PVsyst has been used widely to simulate the PV system analysis and has been accepted by the Government, International Banks, and Academic Institutions to predict, design, and implement the PV system [6]. There were 13 locations used in the region of the OKU administrative area shown in Figure 1. Each location represented each district in the OKU region area where the sampling points were in the center of the village area.

To calculate the number of energy production, the rooftop photovoltaic plant (RPP) was used as the simulation of the SPE harvesting in the studied area. The nominal rating of RPP was designed for most of the residential housing size of Indonesia which was around 45-100 m 2 [17] with a nominal rating of 4800 Watts. To support the nominal rating, 16 units of Jinkosolar JKM 300M-60-V module are used. To support the modules, the rooftop area needs at least 27 m 2 size with a specific tilt angle of the roof around 10 degrees as the optimum tilt angle in Indonesia [18]. There is no specific design of RPP as sunlight irradiation is considered the same level at all modules. To ensure the RPP model implements the regulation of the MEMR, the RPP is related to the grid system managed by PT. Perusahaan Listrik Negara or abbreviated as PLN. Furthermore, the SolarEdge SE4K-EU-APAC was used as an inverter to convert the direct current (DC) to alternating current (AC).

Figure 1. 13 Sampling Point of GHI Assessment in the region of the Ogan Komering Ulu: (1) West Baturaja, (2) East Baturaja, (3) Lubuk Raja, (4) Lubuk Batang, (5) Muara Jaya, (6) Peninjauan, (7) Sosoh Buay Rayap, (8) Ulu Ogan, (9) Sinar Peninjauan, (10) Semidang Aji, (11) Pengandonan, (12) Lengkiti, (13) Kedaton Peninjauan Raya

2.3 Economic analysis of rooftop photovoltaic plant (RPP) model

The economic analysis aims to evaluate the RPP implemented by the two MEMR Regulations. The goals are to see if the Regulations offer economic feasibility that could trigger the investors or people in Indonesia to invest in the RPP. The detail of economic analysis will be described in detail.

2.3.1 Investment and energy cost analysis

In this study, the investment analysis began with the economic analysis of RPP where three (3) parameters were considered: The Capital Expenditure (CAPEXpv), The Operating Expenditure (OPEXpv), and The Levelized Cost of Energy (LCOE). The CAPEXpv counts how much the investment of 4800 Watts RPP costs. The CAPEXpv includes the cost of buying the PV modules C mod , the inverter C ivt accessories and tools C t , cost of installation C ist , insurance cost during the installation and delivery C isr , and tax C tax . All the price was based on the present market and could be changed over time. The detail of the CAPEXpv calculation is shown in Eq. (1).

$\begin{gathered}C A P E X_{p v}=C_{\text {mod }}+C_{i v t}+C_t+C_{i s t}+C_{i s r}  +C_{\text {tax }}(i)\end{gathered}$           (1)

Furthermore, the study analyzed the annual OPEX pv which specifies the Operating and Maintenance (O&M) and the labor costs. The OPEX pv is generally set up to 5% of the CAPEX pv [19, 20]. However, the present study considered a cheaper labor and maintenance cost in the first year to suppress the OPEX pv based on the data in Indonesia. Thus, the OPEX pv was set to 1% of CAPEX pv shown in Eq. (2) which is in accordance with the one reported by a previous study [19]. However, there would be an adjustment cost of OPEX pv annually which the annual OPEX pv price increase was up to 5% annually to cover any financial change during the production period.

$O P E X_{p v}=1 \% C A P E X_{p v}$                (2)

To last investment and energy analysis is the Levelized Cost of Electricity (LCOE) which presents the electricity price generated by the proposed RPP. The LCOE plays a role in determining the financial viability of RNE technologies [20]. The LCOE could be calculated using several ways [21-23]. However, the main idea of calculating the LCOE was to estimate the cost to produce the present electricity over its lifetime production. Thus, we develop Eq. (3) to show the way to calculate the LCOE of RPP for the lifetime period of 25 years [6, 21].

$L C O E=\frac{\sum_{t=1}^n \frac{1+(O P E X p v) t}{(1+r)^t}}{\sum_{t=1}^n \frac{E_t}{(1+r)^t}}$                (3)

where, n refers to the production period (25 years); r equals the discount rate where the present study uses 8% of the discount rate based on the one reported by previous study [24], and t is the year of production.

2.3.2 Economic analysis

The further economic analysis in this section aims to evaluate the E-I electricity regulation as the function of the RPP implementation in Indonesia. There are two main regulations: (1) The MEMR No. 49/2018 Concerning the Uses of Rooftop Solar Electricity Systems by the Consumer of State Electricity Company or PT. Perusahaan Listrik Negara (abbreviated as PT. PLN) and (2) The MEMR No. 26/2021 Concerning the Rooftop Solar Electricity System Connected to Electricity Grid of Holders of Electricity Supply Business License for Public Interest. In general, those two regulations have different regulations about the E-I scenarios where the MEMR No. 49/2018 allows 65% of export-import coverage of RPP while the MEMR No. 26/2021 could fully cover 100% of the E-I electricity of RPP. Therefore, the difference could provide the economic difference which is further analyzed by five (5) most used economic analysis parameters: Net Present Value (NPV), Internal Rate of Return (IRR), Net Benefit Cost Ratio (Net B/C), Return on Investment (ROI), and Payment Back Period (PBP). The detailed calculation for each economic parameter is shown in Eqs. (4)-(8).

$N P V=\frac{(\text { Benefit-Cost })}{(1+r)^n}$                (4)

$\sum\left[\frac{C}{(1+r)}\right]^n=\sum\left[\frac{B}{(1+r)}\right]^n$                (5)

Net $\frac{B}{C}=\frac{\text { Present Value of Benefits PVV }}{\text { Present Value of Cost PV }}$                (6)

ROI $=\frac{\text { Present Return }}{\text { Present Cost }}$                (7)

$P B P=T-1+\frac{\sum_{i=1}^{T-1}(B-C)}{(B-C)_T}$                (8)

where, n refers to the production period where in this study, the production period was limited to 25 years old based on the module performance lifetime warranty [25]; r equals the discount rate where the present study uses 8% of the discount rate based on the one reported by previous study [24]; and T is the first year after the cumulative profit equals to zero (start gaining profit).

To provide an overview of how the E-I scenarios affect the economic analysis, all the production of RPP would be sold to PT. PLN with a specific assumption is shown in Table 1. To compare the E-I scenarios, the present study uses four (4) scenarios of the E-I started by 65% (S1), 70% (S2), 80% (S3), and 100% (S4) to test the sensitivity of the RPP.

Table 1. General assumptions of economic analysis

3.1 Global horizontal irradiation

In general, 13 sampling points show a similar amount of SPE potential shown in Figure 2. The average GHI was approximately around 4.8 kWh/m 2 where the highest GHI was found in the district of Muara Jaya (5.15 kWh/m 2 ) followed by Pengandonan (5.10 kWh/m 2 ), Ulu Ogan (5.08 kWh/m 2 ), and Lengkiti (5.05 kWh/m 2 ) where all the highest GHI located in the Southwest and South part of OKU regency. Talking about the GHI in a year, the GHI in all 13 sampling locations formed a similar pathway where the highest GHI was found in April at the beginning of the dry season in Indonesia. Since Indonesia only has two seasons, the lowest GHI was found in the wet season starting from December to January as the peak of the wet season. Furthermore, the result of GHI was quite like the reports by references where the GHI of the studied area was in the range of 4.4 - 5.0 kWh/m 2 [12, 27].

Assessing the GHI of the studied area confirmed that Indonesia has a high potential in developing SPE. The variation of GHI in each sampling point could provide a different SPE potential. However, the difference between each sampling point is quite low where the difference could be negligible. Furthermore, the SPE potential in the study area is high compared to the general GHI in Indonesia which is around 4.4 - 5.0 kWh/m 2 [12, 27]. The high potential of SPE in the studied area is in accordance with the plan of GORI to choose SPE as the highest-targeted source of RNE in Indonesia. The SPE could contribute up to 47% alone of RNE with the electrical power capacity reached to 207.8 Gigawatts (GW) [13].

3.2 Energy production of Rooftop Photovoltaic Models (RPVM)

The RPP models consist of 16 modules using a Poly-crystalline solar panel by Jinkosolar with a specific power rate of 300 W per module. The optimization of the system was done by the PVsyst software with the output of the system including Energy Output and Loss shown in Table 2.

In Table 2, the proposed RPP model could produce up to 7,200 kilo-watts hour (kWh) of total RNE annually with 1501 kWh/kilo-watts peak (kWp) of annual electrical production. The total energy has exceeded the average electrical consumption of Indonesia's Residences where the average electrical consumption in Indonesia is around 1.06 MWh in 2020 or 1.173 MWh in 2022 [28]. Thus, the RPP could ideally supply the electrical consumption of a household in Indonesia in general. The electrical supply of RPP would be stable every month since Indonesia has geographical advantages located in the equator area with high sunlight exposure every day of the year. Figure 3 shows the electrical production of RPP in every production month.

Table 2. Energy production and system loss

Figure 2. The global horizontal irradiance of 13 sampling points in the Ogan Komering Ulu Region

In technical details, the RPP could still have some losses where Table 2 showed that the highest losses wer coming from temperature loss. The temperature loss could be the ambient temperature or the cell temperature which further affects the performance of the PV modules. The average ambient temperature in Indonesia is around 28℃ with the max ambient temperature reaching 36.3℃ [29]. The ambient temperature in Indonesia (Average and Max Temp) has exceeded the tested temperature of the PV cell which is around 25℃. Thus, the ambient temperature could provide an effect on the RPP installed in Indonesia.

Figure 3. The normalized electrical production of simulated Rooftop Photovoltaic Plant (RPP) system

The other potential temperature loss comes from the internal cell temperature. The cell temperature was gained from the unconverted radiation adsorbed by the cell which further raised the internal temperature of the PV module. Both ambient and internal temperatures at a high-level could lower the efficiency by increasing the internal carrier recombination rate which further lowers the amount of electricity produced [7]. Studies showed that PV panels have a better performance and lifetime period with a cooling process applied [30, 31]. The other option to prevent temperature loss is by designing Floating-Photovoltaic Plant (FPV) [32]. The FPV could have the direct colling system since the module is in the water surface. However, the FPV is designed for the large-scale PV system where it could not be applied in the residential PV system.

The other considered losses on RPP were Inverter Loss due to Active and Reactive Power, Conduction, Switch, and Stray Losses [33] and some other technical losses such as internal losses (IAM factor on global), Irradiance level PV loss due to light penetration, and Ohmic wiring losses (see Table 2). To prevent the inverter and accessories-based losses, the high capacity and high technology inverter could provide a high efficiency conversion from direct current to alternating current [34].

3.3 Economic analysis

The CAPEX pv of RPP was approximately IDR. 81.150.000 with the cost details shown in Table 3. Based on the CAPEX pv and the installed power capacity, the value of PV investment per watt of RPP was around IDR 16,900 (USD 1.13 with an exchange rate of IDR 14.956). Modules and Inverter still cost the highest investment where approximately 75% of RPP investment comes from the module and inverter costs. However, the investment cost of RPP was half price compared to the investment cost in the United States where the RPP per watts of power cost around USD 2.71 per watts [35].

The OPEX pv of RPP in the present study was cost around IDR. 8.150.000 in the first year with an annual tax adjustment of around 5%. Most of the owners of RPP would use a third party such as a technical or private company to do the O&M of the RPP. In general, the OPEX pv tends to offer medium to large-sized photovoltaic models with a minimum capacity of around 500 kilowatts of power generated. The main reason is most of the owners do not have the resources to operate and maintain the RPP or the owner is not ready to make a long-term capital investment in RPP [36]. However, the present study adds the OPEX pv as the option to estimate the capital investment on the RPP.

The levelized cost of electricity of RPP was shown in Table 3. The LCOE of RPP cost IDR. 1335.8 (USD 0.089 or 8.9 cents) per kWh which is lower than the feed-in tariff used in the present study. The LCOE obtained in the present study is in accordance with the one reported by IESR [37]. Indonesian Power, a company that focused on RNE, believes that the LCOE of RPP could be suppressed to 3.7 cents per kWh [38]. The lower LCOE could be reached in the future because of the cheaper PV modules and inverters.

Table 3. Investment and cost output

Five economic parameters were used in the present study. Figure 4 shows the Net Present Value (NPV) of the RPP with the different four (4) scenarios. In general, all the scenarios obtained a positive NPV meaning that all scenarios provide profitability or form a good value of investment. To be more specific, the main difference was the year when the RPP started gaining profit. S4 scenarios would produce the positive NPV after the twelfth (12 th ) years of production time which was the almost half period of RPP lifetimes. The result could be a simple math, but it could mean that revising the E-I scenarios to 100%, could make the RPP less risky compared to the other E-I scenarios. In comparison, the first scenario (S1) would not gain any interest since it generates the lowest NPV. By revising the E-I scenario from 65% to 100%, the NPV rises from IDR. 1.8 million (S1) to IDR. 53.9 million (S4).

The result of the NPV also supports the other four economic parameters shown in Table 4. The highest NPV generated by the scenario by MEMR No. 26/2021 was obtained from the highest improvement in Net B/C value and ROI of the fourth scenario (S4). The revision of E-I from 65% to 100% improves the value of Net B/C and ROI to approximately 63% and 21.3%, respectively. The result showed that the owner of RPP could still return more than half investment at the end of production.

Figure 4. Net present value of four (4) scenarios of export-import subsidies of rooftop photovoltaic plant in Indonesia

Table 4. Economic and sensitivity test

However, the IRR and PBP showed a slight improvement whereas the PBP showed the lowest effect on the E-I change. The route improvement of IRR values for each scenario is in accordance with the NPV result where the RPP provides an economic advantage since the IRR value of each scenario performs a value larger than 8% or larger than the interest rate of any bank in Indonesia [24]. This means that the RPP could provide additional profitability compared to depositing the same amount of money in any bank in Indonesia.

In terms of PBP, there is no significant difference between each scenario where the PBP is only short for a couple of months by changing the E-I scenario. The PBP was a half-time period of lifetime production indicating the RPP investment was less risky and regarded as appropriate for investment [6]. In the economic conclusion, the result confirmed that by revising the MEMR No. 49/2018 to the MEMR No. 26/2021 could provide a high improvement and advantages from the economic point of view especially the NPV, Net B/C, and ROI.

Techno-economic analysis of RPP based on two Ministerial Energy and Mineral Resource have been successfully made. The result in technical details demonstrated that SPE in the studied area is high and RPP could be an ideal technology to supply the daily electrical demand of society in all production years. In economic details, the changes in the regulation about RPP from MEMR from 49/2018 to MEMR 26/2021 about Export – Import Subsidies made a huge difference since the new MEMR provided a better economic advantage such as positive NPV which made RPP become the less risky project. The other advantages are the high improvement in Net B/C and ROI where the improvement is 63% and 21.3%, respectively, compared to the condition with MEMR 49/2018. However, IRR and PBP are slightly improved by changing the new regulation on E-I scenarios. The economic analysis also showed that RPP could provide better profitability compared to depositing the same amount of money in any bank in Indonesia. The result confirmed that the investing in RPP is profitable and less risky project and performing new regulation of MEMR 26/2021 is a smart movement from GORI to stimulate the growth of green energy in Indonesia. Furthermore, the financial regulation such as bankable investment regulation and green investment mechanism should be made as soon as possible to provide a financial pathway for the people in Indonesia to actively participate in the RPP investment. On the other hand, the creative and effective dissemination should be made to ensure everyone knows the advantages of transitioning energy from fossil-based energy to green energy. The combination between good regulation and implementation could make the target of 3.6 GW of SPE in RNE Mix in 2025 possible.

The authors express gratitude to the Department of Energy and Mineral Resources of South Sumatra for granting access to conduct the research.

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ORIGINAL RESEARCH article

This article is part of the research topic.

Mathematical Approaches in Advancing Medical Science, Physical Fitness, and Clinical Sciences

Investigation Toward The Economic Feasibility of Personalized Medicine For Healthcare Service Providers: The Case of Bladder Cancer Provisionally Accepted

• 1 Ariel University, Israel

The final, formatted version of the article will be published soon.

In today's complex healthcare landscape, the pursuit of delivering optimal patient care while navigating intricate economic dynamics poses a significant challenge for healthcare service providers (HSPs). In this already complex dynamic, the emergence of clinically promising personalized medicine-based treatment aims to revolutionize medicine. While personalized medicine holds tremendous potential for enhancing therapeutic outcomes, its integration within resource-constrained HSPs presents formidable challenges. In this study, we investigate the economic feasibility of implementing personalized medicine. The central objective is to strike a balance between catering to individual patient needs and making economically viable decisions. Unlike conventional binary approaches to personalized treatment, we propose a more nuanced perspective by treating personalization as a spectrum. This approach allows for greater flexibility in decision-making and resource allocation. To this end, we propose a mathematical framework to investigate our proposal, focusing on Bladder Cancer (BC) as a case study. Our results show that while it is feasible to introduce personalized medicine, a highly efficient but highly expensive one would be short-lived relative to its less effective but cheaper alternative as the latter can be provided to a larger cohort of patients, optimizing the HSP's objective better.

Keywords: personalized medicine, healthcare economics, patient-centric care, resource-constrained healthcare, healthcare systems management

Received: 20 Feb 2024; Accepted: 26 Apr 2024.

Copyright: © 2024 Savchenko and Bunimovich. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Elizaveta Savchenko, Ariel University, Ariel, Israel

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INFO 200 – Blog Post #4 – The Importance of Corporate Archives to Economic and Business History: A Case Study

Case Study Citation:

Valle Coberio, C. G. (2012). The importance of corporate archives to economic and business history: A case study. Society of American Archivists. https://www2.archivists.org/sites/all/files/CaioCoberio-SAA-ResearchReport-2011-Final.pdf

About the Author:

Caio Graco Valle Cobério is the author of the case study titled ‘The Importance of Corporate Archives to Economic and Business History: A Case Study.’ Caio Graco Valle Cobério earned his bachelor’s degree in History from the Faculty of Philosophy and Human Sciences from the Federal University of Minas Gerais. Valle Cobério has a postgraduate degree specializing in Collections, Teaching, and Cultural Heritage from the Regional University of Blumenau. He earned his master’s degree in History of the Present Time from the State University of Santa Catarina. Valle Cobério received his Ph.D. in Economic History from the University of Sao Paulo. He served at the Public Archives of Sao Paulo and taught history, philosophy, sociology, and other related subjects in various public schools in Santa Catarina. Caio Graco Valle Cobério wrote this case study while working towards his doctorate degree (Fisica, 2022).

Scopes of the Case Study:

Caio Graco Valle Cobério’s case study titled ‘The Importance of Corporate Archives to Economic and Business History: A Case Study’ describes how archival records are raw material given to archivists to help preserve the historical narrative. “Archival programs in corporations are necessary to the correct maintenance of business records” (Valle Cobério, 2012). Caio Graco Valle Cobério studies the history of corporate archives, and how the realization of a need for business histories did not develop in the United States until the late nineteenth century. He discusses how business historians and archivists concentrate on different factors, including the corporations’ economic circumstances, markets, systems, and businesses. Their information needs are a result of “an investigation made with professionalism and methodical procedures” (Valle Cobério, 2012). This case study evaluates how businesses comprehended their history in the United States throughout the 20th century “in order to build knowledge in the field of Corporate Archives” (Valle Cobério, 2012). Caio Graco Valle Cobério reviews when businesses have started to preserve their history and how it has developed over time in the United States. He discusses the different points of views that various authors have regarding the importance of archiving business and economic history (Valle Cobério, 2012).

Methodology and Findings of the Case Study:

In his study, Caio Graco Valle Cobério discusses how while historical methods are important for historical knowledge, the preservation of records in corporate archives should also be done using archival methods (Valle Cobério, 2012). “The concept itself implies methodology” (Valle Cobério, 2012) because “careful selection is required if the records of business and industry are to be both management tool and historical resource” (Eulenberg, 1984). By reviewing various points of views on the importance of archiving business and economic history of businesses and discussing the history of preserving this information in the United States, Caio Graco Valle Cobério was able to prove the importance of corporate archivists and preserving the history of corporations and businesses.  He concludes that there is a need for solid archival programs in corporations that are capable of identifying the availability of records to research  (Valle Cobério, 2012). 

“An industrial society like ours, which is spreading on the Earth since the 19th century, has generated a form of organization that occupies a central position in the system, bringing cultural, social, compartmental influences and much more: this organization is the large business, the national and multinational corporations. Everyone needs to get information of how they work, and such consummation shall be provided through the records, which are ones of the best means to reflect the reality with some objectively, so much necessary to gain a correct understanding.” (Valle Cobério, 2012).

At the end of Coberio’s case study, he provides an appendix chart that displays common items and materials that a corporation preserves, including materials relating to publicizing, operational, communication, human resources, assets and patrimony, and financial and budgetary  (Valle Cobério, 2012). In my previous blog post, I included this appendix chart of long term preservation records in Corporate Archives, and I will include it here as we are now looking at his study more in depth. Caio Graco Valle Cobério adapted this table by extracting information from the literature that he reviewed for this case study (Valle Cobério, 2012).  

Applying this Case Study to the Information Community of Corporate Archivists

This case study is valuable to the information community of corporate archivists because the findings of Caio Graco Valle Cobério’s study show that there is a need for corporate archivists, and that trained archivists can bring additional practices and knowledge to a corporation that historians do not have (Valle Cobério, 2012). Businesses and corporations are in need of archival programs that are capable of identifying the availability of records to research, and corporate archivists are the individuals who are able to do this. By reflecting on the history of businesses and how they have preserved their history, corporate archivists can see that the work they do is important and needed. The appendix chart that Caio Graco Valle Cobério includes in his study is also valuable information to corporate archivists because it provides a guide as to what information corporations should be saving and for how long (Valle Cobério, 2012).

References:

Física, P. (2022, Sept 4). Caio Graco Valle Cobério. Escavador. https://www.escavador.com/sobre/593992/caio-graco-valle-coberio

Eulenberg, J. N. (1984). The corporate archives: Management tool and historical resource. The Public Historian, 6 (1), pp. 20-37. https://doi.org/10.2307/3377682

Valle Cobério, C. G. (2012). The importance of corporate archives to economic and business history: A case study. Society of American Archivists. https://www2.archivists.org/sites/all/files/CaioCoberio-SAA-ResearchReport-2011-Final.pdf

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