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An analytical formula is presented for the Social Cost of Carbon (SCC) taking account of intragenerational income inequality, in addition to intergenerational income inequality, macro-economic uncertainty and rare disasters to economic growth. The social discount rate is adjusted for intra- and intergenerational inequality aversion and risk aversion. If growth reduces intragenerational inequality, the SCC is lower than with inequality-neutral growth, especially if intra- and intergenerational inequality aversion are high. Calibrated to the observed interest rate and risk premium, the SCC in 2020 is $125/tCO2 without considering intragenerational inequality, $81/tCO2 if intragenerational inequality decreases over time, as a continuation of historical trends suggests (based on Shared Socioeconomic Pathway (SSP) 2), and $213/tCO2 if inequality increases (SSP4). Intragenerational inequality has a similar order of effect on the SCC as accounting for rare macroeconomic disasters.

A formula is derived for the social cost of carbon (SCC) that takes account of intragenerational income inequality and its evolution with economic growth. The social discount rate (SDR) should be adjusted to account for intragenerational and intergenerational inequality aversion and for risk aversion. If growth increases (reduces) intra-generational inequality, the SDR is lower (higher) and the SCC higher (lower) than along an inequality-neutral growth path, especially if intra-generational and intergenerational inequality aversion are higher. The same qualitative result is shown for two welfare specifications, one with a representative agent with equally distributed equivalent (EDE) income and the other considers individuals separately across the income distribution. The latter specification causes an additional impact of income inequality on the SDR and SCC because individuals are compared both within and between time periods. Our preferred EDE calibration to a scenario in which global intragenerational inequality declines over time, leads to a SCC in 2020 of $70/tCO2 compared to a value of $85/tCO2 without the effect of inequality.

Integrated assessment models (IAMs) have emerged as key tools for building and assessing long term climate mitigation scenarios. Due to their central role in the recent IPCC assessments, and international climate policy analyses more generally, and the high uncertainties related to future projections, IAMs have been critically assessed by scholars from different fields receiving various critiques ranging from adequacy of their methods to how their results are used and communicated. Although IAMs are conceptually diverse and evolved in very different directions, they tend to be criticised under the umbrella of 'IAMs'. Here we first briefly summarise the IAM landscape and how models differ from each other. We then proceed to discuss six prominent critiques emerging from the recent literature, reflect and respond to them in the light of IAM diversity and ongoing work and suggest ways forward. The six critiques relate to (a) representation of heterogeneous actors in the models, (b) modelling of technology diffusion and dynamics, (c) representation of capital markets, (d) energy-economy feedbacks, (e) policy scenarios, and (f) interpretation and use of model results.

Integrated assessment models (IAMs) have emerged as key tools for building and assessing long term climate mitigation scenarios. Due to their central role in the recent IPCC assessments, and international climate policy analyses more generally, and the high uncertainties related to future projections, IAMs have been critically assessed by scholars from different fields receiving various critiques ranging from adequacy of their methods to how their results are used and communicated. Although IAMs are conceptually diverse and evolved in very different directions, they tend to be criticised under the umbrella of 'IAMs'. Here we first briefly summarise the IAM landscape and how models differ from each other. We then proceed to discuss six prominent critiques emerging from the recent literature, reflect and respond to them in the light of IAM diversity and ongoing work and suggest ways forward. The six critiques relate to (a) representation of heterogeneous actors in the models, (b) modelling of technology diffusion and dynamics, (c) representation of capital markets, (d) energy-economy feedbacks, (e) policy scenarios, and (f) interpretation and use of model results.

openaire: EC/H2020/821124/EU//NAVIGATE Integrated assessment models (IAMs) form a prime tool in informing about climate mitigation strategies. Diagnostic indicators that allow comparison across these models can help describe and explain differences in model projections. This increases transparency and comparability. Earlier, the IAM community has developed an approach to diagnose models (Kriegler (2015 Technol. Forecast. Soc. Change 90 45-61)). Here we build on this, by proposing a selected set of well-defined indicators as a community standard, to systematically and routinely assess IAM behaviour, similar to metrics used for other modeling communities such as climate models. These indicators are the relative abatement index, emission reduction type index, inertia timescale, fossil fuel reduction, transformation index and cost per abatement value. We apply the approach to 17 IAMs, assessing both older as well as their latest versions, as applied in the IPCC 6th Assessment Report. The study shows that the approach can be easily applied and used to indentify key differences between models and model versions. Moreover, we demonstrate that this comparison helps to link model behavior to model characteristics and assumptions. We show that together, the set of six indicators can provide useful indication of the main traits of the model and can roughly indicate the general model behavior. The results also show that there is often a considerable spread across the models. Interestingly, the diagnostic values often change for different model versions, but there does not seem to be a distinct trend.

The COVID-19 pandemic has affected households across the globe due to the health impacts but also through indirect socioeconomic effects as a result of the additional stress on the health systems, implications of the lockdowns and other policy measures undertaken by governments. Moreover, there is evidence that these impacts are associated with socioeconomic characteristics of households and could lead to an increase in inequality and poverty. In this paper, we conduct a first assessment based on household surveys in a large set of countries and analyze the determinants of income shocks at the household-level and macroeconomic inequality implications. While the average income losses of on average 4% but up to 27% are already high (similar to GDP losses in 2020 of on average 6% in our sample), we find an even more striking increase in inequality, of up to several points (1.2 points on average) of the typically very flat Gini index. Across countries, we find that on average an additional one percentage point loss in GDP was associated with an increase in theGini by one percentage point. Analyzing the determinants of the income shock, we find strong evidence of heterogeneity with higher likelihood of income shocks for poorer, female-led, and less-educated households. The results indicate that we are experiencing an unprecedented crisis also in terms of economic inequality. The policy response to limit the macroeconomic repercussions therefore should explicitly include reducing inequality. Otherwise, a rebuilt macro-economy, will exhibit a much higher degree of social and economic inequality that are likely to persist.

The consensus view amongst economists is that carbon prices, in order to be effcient, must be the same across the globe. But when there are inefficiencies in the allocation of capital so that consumers in different countries face different discount rates, we show that efficient carbon prices must be different across countries. This is a consequence of Hotelling's familiar argument on the price of a non-renewable resource: it must grow at the rate of the next best use of marginal funds, which is equal to the country's discount rate. If different countries discount at different rates, their carbon prices ought to grow at different rates as well. If they grow at different rates, they can't be the same all of the time, as first-best carbon prices are. The computational climate policy literature has so far avoided this conclusion by altering time preferences in a country specific way through time-varying Negishi weights. We show that the use of such weights causes inefficient policy prescriptions and, furthermore, has the particularly undesirable consequence of incorrectly discounting future consumption more in countries with high growth rates. The existence of inefficiencies in the savings process - causing differences in discount rates - is well-known and should be acknowledged head on in climate policy analysis. Doing so results in global mitigation policy with carbon price paths for different countries growing (efficiently) at different rates.

Projections of energy intensity are important for the assessment of future energy demand, future emission pathways, and the costs of climate policies. We estimate and simulate energy intensity based on a conditional convergence approach, and show how based on the results the long-run minimum of energy intensity attainable can be estimated. We consider education, urbanization, and institutional factors and ftnd them to positively impact energy intensity improvements. We link the estimated econometric models to an iterative projection model, resulting in a ftnite long-term lower limit of energy intensity of GDP to be around 0:35MJ/$ at the global level in most SSPs. Yet, by 2100, we estimated that energy intensity below one is hard to achieve based on historical patterns. By 2100, the projected energy intensities are in the range of 1MJ/$ at the global level. These results show that scenarios such as the ones used in the SR15 can be rationalized based on empirically founded projections, and that in particular the very low energy demand scenarios can be considered feasible on empirical grounds. The speed at which such ow values are achievable is however the big question and achieving them will require substantially going beyond historical technical change patterns.

Cost-effective achievement of the Paris Agreement's long-term goals requires the unanimous phase-out of coal power generation by mid-century. However, continued investments in coal power plants will make this transition difficult. India is one of the major countries with significant under construction and planned increase in coal power capacity. To ascertain the likelihood and consequences of the continued expansion of coal power for India's future mitigation options, we use harmonised scenario results from national and global models along with projections from various government reports. Both these approaches estimate that coal capacity is expected to increase until 2030, along with rapid developments in wind and solar power. However, coal capacity stranding of the order of 133–237 GW needs to occur after 2030 if India were to pursue an ambitious climate policy in line with a well-below 2 °C target. Earlier policy strengthening starting after 2020 can reduce stranded assets (14–159 GW) but brings with it political economy and renewable expansion challenges. We conclude that a policy limiting coal plants to those under construction combined with higher solar targets could be politically feasible, prevent significant stranded capacity, and allow higher mitigation ambition in the future.

Many countries have implemented national climate policies to accomplish pledged Nationally Determined Contributions and to contribute to the temperature objectives of the Paris Agreement on climate change. In 2023, the global stocktake will assess the combined effort of countries. Here, based on a public policy database and a multi-model scenario analysis, we show that implementation of current policies leaves a median emission gap of 22.4 to 28.2 GtCO2eq by 2030 with the optimal pathways to implement the well below 2 °C and 1.5 °C Paris goals. If Nationally Determined Contributions would be fully implemented, this gap would be reduced by a third. Interestingly, the countries evaluated were found to not achieve their pledged contributions with implemented policies (implementation gap), or to have an ambition gap with optimal pathways towards well below 2 °C. This shows that all countries would need to accelerate the implementation of policies for renewable technologies, while efficiency improvements are especially important in emerging countries and fossil-fuel-dependent countries.

Mitigation pathways allowing for temperature overshoot often ignore the related climate and macroeconomic impacts. Net-zero pathways with limited overshoot could reduce low-probability high-consequence risks and economic loss. Mitigation pathways exploring end-of-century temperature targets often entail temperature overshoot. Little is known about the additional climate risks generated by overshooting temperature. Here we assessed the benefits of limiting overshoot. We computed the probabilistic impacts for different warming targets and overshoot levels on the basis of an ensemble of integrated assessment models. We explored both physical and macroeconomic impacts, including persistent and non-persistent climate impacts. We found that temperature overshooting affects the likelihood of many critical physical impacts, such as those associated with heat extremes. Limiting overshoot reduces risk in the right tail of the distribution, in particular for low-temperature targets where larger overshoots arise as a way to lower short-term mitigation costs. We also showed how, after mid-century, overshoot leads to both higher mitigation costs and economic losses from the additional impacts. The study highlights the need to include climate risk analysis in low-carbon pathways.

In the version of ‘Supplementary Data 1’ originally published with this Article, the units for the ‘Capacity|Electricity|*’ variables in the ‘Non_Investment_Annual’ tab were incorrectly given as EJ/yr; they should have read GW. This has now been corrected. Also, some of the variables listed in the ‘Non_Investment_Variable_Defs’ were not required and have therefore been removed.

Over the last few decades, integrated assessment models (IAM) have provided insight into the relationship between climate change, economy, and climate policies. The limitations of these models in capturing uncertainty in climate parameters, heterogeneity in damages and policies, have given rise to skepticism about the relevance of these models for policy making. IAM community needs to respond to these critics and to the new challenges posed by developments in the policy arena. New climate targets emerging from the Paris Agreement and the uncertainty about the signatories’ commitment to Nationally Determined Contributions (NDCs) are prime examples of challenges that need to be addressed in the next generation of IAMs. Given these challenges, calculating the social cost of carbon requires a new framework. This can be done by computing marginal abatement cost in cost-effective settings which provides different results than those calculated using constrained cost-benefit analysis. Here we focus on the areas where IAMs can be deployed to asses uncertainty and risk management, learning, and regional heterogeneity in climate change impacts.

The Paris Agreement has set stringent temperature targets to limit global warming to 2°C above preindustrial level, with efforts to stay well below 2°C. At the same time, its bottom-up approach with voluntary national contributions makes the implementation of these ambitious targets particularly challenging. Climate engineering – both through carbon dioxide removal (CDR) and solar radiation management (SRM) – is currently discussed to potentially complement mitigation and adaptation. Results from integrated assessment models already suggest a significant role for some forms of climate engineering in achieving stringent climate objectives1. However, these estimates and their underlying assumptions are uncertain and currently heavily debated2–4. By reviewing the existing literature and reporting the views of experts, we identify research gaps and priorities for improving the integrated assessment of climate engineering. Results point to differentiated roles of CDR and SRM as complementary strategies to the traditional ones, as well as diverse challenges for an adequate representation in integrated assessment models. We identify potential synergies for model development which can help better represent mitigation and adaptation challenges, as well as climate engineering.

The Paris Agreement has set stringent temperature targets to limit global warming to 2°C above preindustrial level, with efforts to stay well below 2°C. At the same time, its bottom-up approach with voluntary national contributions makes the implementation of these ambitious targets particularly challenging. Climate engineering – both through carbon dioxide removal (CDR) and solar radiation management (SRM) – is currently discussed to potentially complement mitigation and adaptation. Results from integrated assessment models already suggest a significant role for some forms of climate engineering in achieving stringent climate objectives1. However, these estimates and their underlying assumptions are uncertain and currently heavily debated2–4. By reviewing the existing literature and reporting the views of experts, we identify research gaps and priorities for improving the integrated assessment of climate engineering. Results point to differentiated roles of CDR and SRM as complementary strategies to the traditional ones, as well as diverse challenges for an adequate representation in integrated assessment models. We identify potential synergies for model development which can help better represent mitigation and adaptation challenges, as well as climate engineering.

Inequity plays a fundamental role in the evaluation of social welfare in many dimensions. We revisit the concept of inequity, whether across states of world (uncertainty), across individuals (inequality) and across generations (intergenerational equity), using a common framework generalizing the discounted expected utilitarianism approach. We propose a general measure of welfare as equity equivalents and develop the corresponding inequity index. We then allow for different degrees of inequity aversion across the three dimensions to span a simplex of possible inequity preferences and relate it to the recent literature on this topic. We show that the ordering of aggregation across the different dimensions matters for welfare evaluations and that many welfare-theoretical approaches developed in the literature may be seen as special cases of this general framework.

The Paris Agreement has set stringent temperature targets to limit global warming to 2°C above preindustrial level, with efforts to stay well below 2°C. At the same time, its bottom-up approach with voluntary national contributions makes the implementation of these ambitious targets particularly challenging. Climate engineering – both through carbon dioxide removal (CDR) and solar radiation management (SRM) – is currently discussed to potentially complement mitigation and adaptation. Results from integrated assessment models already suggest a significant role for some forms of climate engineering in achieving stringent climate objectives1. However, these estimates and their underlying assumptions are uncertain and currently heavily debated2–4. By reviewing the existing literature and reporting the views of experts, we identify research gaps and priorities for improving the integrated assessment of climate engineering. Results point to differentiated roles of CDR and SRM as complementary strategies to the traditional ones, as well as diverse challenges for an adequate representation in integrated assessment models. We identify potential synergies for model development which can help better represent mitigation and adaptation challenges, as well as climate engineering.

The mismatch between actions to combat climate change, which are based on voluntary national initiatives of limited effort, and the recognition of the importance of global warming is growing. Climate engineering via solar radiation management has been proposed as a possible complement to traditional climate policies. However, climate engineering entails specific risks, including its governance. Free driving, the possibility of unilateral climate engineering to the detriment of other nations, has been recently proposed as a potentially powerful additional externality to the traditional free riding one (Weitzman, 2015). This paper provides the first quantitative evaluation of the risks of free driving. Our results indicate that in a strategic setting there is significant over-provision (by almost an order of magnitude) of climate engineering above what is socially optimal, resulting in a sub-optimal global climate. Regions with high climate change impacts, most notably India and developing Asia, deploy climate engineering at the expenses of other regions.

The mismatch between actions to combat climate change, which are based on voluntary national initiatives of limited effort, and the recognition of the importance of global warming is growing. Climate engineering via solar radiation management has been proposed as a possible complement to traditional climate policies. However, climate engineering entails specific risks, including its governance. Free driving, the possibility of unilateral climate engineering to the detriment of other nations, has been recently proposed as a potentially powerful additional externality to the traditional free riding one (Weitzman, 2015). This paper provides the first quantitative evaluation of the risks of free driving. Our results indicate that in a strategic setting there is significant over-provision (by almost an order of magnitude) of climate engineering above what is socially optimal, resulting in a sub-optimal global climate. Regions with high climate change impacts, most notably India and developing Asia, deploy climate engineering at the expenses of other regions.

Climate Engineering, and in particular Solar Radiation Management (SRM) has become a widely discussed climate policy option to study in recent years. However, its potentially strategic nature and unforeseen side effects provide major policy and scientific challenges. We study the role of the SRM implementation and its strategic dimension in a model with two heterogeneous countries with the notable feature of model misspecification on the impacts from SRM. We find that deep uncertainty leads to a reduction in SRM deployment both under cooperation and strategic behavior, which is a more relevant issue if countries act strategically. Furthermore, we demonstrate that the heterogeneity in impacts from SRM has an asymmetric effect on the optimal policy and could typically lead to unilateral SRM implementation. We also consider heterogeneous degrees of ambiguity aversion, in which case the more confident country only will use SRM.

This paper presents a novel way to disentangle inequality aversion over time from inequality aversion between regions in the computation of the Social Cost of Carbon. Our approach nests a standard efficiency based Social Cost of Carbon estimate and an equity weighted Social Cost of Carbon estimate as special cases. We also present a methodology to incorporate more fine grained regional resolutions of income and damage distributions than typically found in integrated assessment models. Finally, we present quantitative estimates of the Social Cost of Carbon that use our disentangling of different types of inequality aversion. We use two integrated assessment models (FUND and RICE) for our numerical exercise to get more robust findings. Our results suggest that inequality considerations lead to a higher (lower) SCC values in high (low) income regions relative to an efficiency based approach, but that the effect is less strong than found in previous studies that use equity weighting. Our central estimate is that the Social Cost of Carbon increases roughly by a factor of 2.5 from a US perspective when our disentangled equity weighting approach is used.

This paper presents a novel way to disentangle inequality aversion over time from inequality aversion between regions in the computation of the Social Cost of Carbon. Our approach nests a standard efficiency based Social Cost of Carbon estimate and an equity weighted Social Cost of Carbon estimate as special cases. We also present a methodology to incorporate more fine grained regional resolutions of income and damage distributions than typically found in integrated assessment models. Finally, we present quantitative estimates of the Social Cost of Carbon that use our disentangling of different types of inequality aversion. We use two integrated assessment models (FUND and RICE) for our numerical exercise to get more robust findings. Our results suggest that inequality considerations lead to a higher (lower) SCC values in high (low) income regions relative to an efficiency based approach, but that the effect is less strong than found in previous studies that use equity weighting. Our central estimate is that the Social Cost of Carbon increases roughly by a factor of 2.5 from a US perspective when our disentangled equity weighting approach is used.

This paper describes the WITCH - World Induced Technical Change Hybrid - model in its structure, calibration, and the implementation of the SSP/RCP scenario implementation. The WITCH model is a regionally disaggregated hard-linked model based on a Ramsey type optimal growth model and a detailed bottom-up energy sector model. A particular focus of the model is the modeling or technical change and RnD investments and the analysis of cooperative and non-cooperative climate policies. Moreover, the WITCH 2016 version now includes land-use change modeling based on the GLOBIOM model, and air pollutants, as well as detailed modeling of the transport sector and the possibility for stochastic modeling. This version has been also used to implement the Shared Socioeconomic Pathways (SSPs) set of scenarios and RCP based climate policies to provide a new set of climate scenarios. In this paper, we describe in detail the mathematical formulation of the WITCH model, the solution method and calibration, as well as the implementation of the five SSP scenarios. This report therefore provides detailed information for interested users of the model, and for understanding the implementation of the different “worlds" of the SSP.

Climate Engineering, and in particular Solar Radiation Management (SRM) has become a widely discussed climate policy option to study in recent years. However, its potentially strategic nature and unforeseen side effects provide major policy and scientific challenges. We study the role of the SRM implementation and its strategic dimension in a model with two heterogeneous countries with the notable feature of model misspecification on the impacts from SRM. We find that deep uncertainty leads to a reduction in SRM deployment both under cooperation and strategic behavior, which is a more relevant issue if countries act strategically. Furthermore, we demonstrate that the heterogeneity in impacts from SRM has an asymmetric effect on the optimal policy and could typically lead to unilateral SRM implementation. We also consider heterogeneous degrees of ambiguity aversion, in which case the more confident country only will use SRM.

This paper describes the WITCH - World Induced Technical Change Hybrid - model in its structure, calibration, and the implementation of the SSP/RCP scenario implementation. The WITCH model is a regionally disaggregated hard-linked model based on a Ramsey type optimal growth model and a detailed bottom-up energy sector model. A particular focus of the model is the modeling or technical change and RnD investments and the analysis of cooperative and non-cooperative climate policies. Moreover, the WITCH 2016 version now includes land-use change modeling based on the GLOBIOM model, and air pollutants, as well as detailed modeling of the transport sector and the possibility for stochastic modeling. This version has been also used to implement the Shared Socioeconomic Pathways (SSPs) set of scenarios and RCP based climate policies to provide a new set of climate scenarios. In this paper, we describe in detail the mathematical formulation of the WITCH model, the solution method and calibration, as well as the implementation of the five SSP scenarios. This report therefore provides detailed information for interested users of the model, and for understanding the implementation of the different “worlds" of the SSP.

Developing Asia has the world's fastest greenhouse gas emissions growth. This study uses an economy-energy-climate model to assess the effects of Paris Agreement pledges on Asia, in comparison with business as usual (BAU) and more ambitious scenarios. Results confirm that pledges must be strongly increased in ambition to achieve the Paris Agreement's goal of less than 2 degrees Celsius (2êC) warming. The policy costs of Asia's pledges are found to be less than 1% of gross domestic product (GDP) through 2050, while 2êC scenarios may cost less than 2% of GDP. However, costs are sensitive to assumptions about international carbon markets and mitigation timing, with costs for 2êC scenarios doubling in the absence of carbon trade, and increasing the later that mitigation is initiated. Under the 2êC scenarios, annual average energy supply investments are about $300 billion above the BAU levels through 2050. Mitigation policy may substantially reduce air pollution mortality, with up to 600,000 fewer deaths in Asia annually by 2050. When costs, benefits of avoided climate change, and cobenefits are considered together, investment in mitigation policy is found to have substantial economic returns for the region - if action is taken rapidly and international carbon market mechanisms are implemented.

The Paris Agreement reinforced the objective of keeping global temperature rise well below 2°C, and of pursuing efforts to limit the temperature increase even further to 1.5°C above pre-industrial levels. Such low stabilization requires swift action and an almost full-scale decarbonization of energy systems worldwide. Over the past four years ADVANCE has improved Integrated Assessment Models (IAM) to better quantify the requirements for climate stabilization and the implications of international climate agreements, including the implications of the Paris Climate Agreement.

This paper studies the optimal extraction of a non-renewable resource under uncertainty using a discrete-time approach in the spirit of the literature on precautionary savings. We find that boundedness of the utility function, in particular the assumption about U(0), gives very different results in the two settings which are often considered as equivalent. For a bounded utility function, we show that in a standard two-period setting, prudence is no longer sufficient to ensure a more conservationist extraction policy than under certainty. If on the other hand we increase the number of periods to infinity, we find that prudence is not anymore not anymore necessary to induce a more conservationist extraction policy and risk aversion is sufficient. These results highlight the importance of the specification of the utility function and its behavior at the point of origin.

This paper studies the optimal extraction of a non-renewable resource under uncertainty using a discrete-time approach in the spirit of the literature on precautionary savings. We find that boundedness of the utility function, in particular the assumption about U(0), gives very different results in the two settings which are often considered as equivalent. For a bounded utility function, we show that in a standard two-period setting, prudence is no longer sufficient to ensure a more conservationist extraction policy than under certainty. If on the other hand we increase the number of periods to infinity, we find that prudence is not anymore not anymore necessary to induce a more conservationist extraction policy and risk aversion is sufficient. These results highlight the importance of the specification of the utility function and its behavior at the point of origin.

Uncertainty is prevalent in the context of climate change impacts. Moreover, the distribution across the globe is not uniform. We analyze how climate risks could be reduced via an insurance scheme at the global scale across regions and quantify the potential welfare gains from such a scheme. Starting from the standard welfare analysis in Integrated Assessment Models (IAMs), which assumes no risk sharing across region, we introduce global risk sharing via a market for state-dependent Arrow-Debreu securities. We show that this allows equalizing relative consumption differences between states of the world across regions. We estimate that such risk sharing scheme of climate risks could lead to welfare gains reducing the global costs of climate change by up to one third, while the amount of transfers required is substantial. This provides arguments for considering risk sharing in IAMs, but also for potentially welfare increasing negotiations about sharing risks of climate change at the global level.

In this paper we report results from a comparison of numerically calibrated game theoretic integrated assessment models that explore stability and performance of international coalitions for climate change mitigation. Specifically, by means of this ensemble of models we are able to identify robust results concerning incentives of nations to commit themselves to a climate agreement, and to estimate what stable agreements can achieve in terms of greenhouse gas mitigation. We also assess the potential of transfers that redistribute the surplus of cooperation in order to foster stability of climate coalitions. In contrast to much of the existing analytical game theoretical literature, we find substantial scope for self-enforcing climate coalitions in most models that close much of the abatement and welfare gap between complete absence of cooperation and full cooperation. This more positive message follows from the use of transfer schemes that are designed to counteract free riding incentives.

Uncertainty is prevalent in the context of climate change impacts. Moreover, the distribution across the globe is not uniform. We analyze how climate risks could be reduced via an insurance scheme at the global scale across regions and quantify the potential welfare gains from such a scheme. Starting from the standard welfare analysis in Integrated Assessment Models (IAMs), which assumes no risk sharing across region, we introduce global risk sharing via a market for state-dependent Arrow-Debreu securities. We show that this allows equalizing relative consumption differences between states of the world across regions. We estimate that such risk sharing scheme of climate risks could lead to welfare gains reducing the global costs of climate change by up to one third, while the amount of transfers required is substantial. This provides arguments for considering risk sharing in IAMs, but also for potentially welfare increasing negotiations about sharing risks of climate change at the global level.

In this paper we report results from a comparison of numerically calibrated game theoretic integrated assessment models that explore stability and performance of international coalitions for climate change mitigation. Specifically, by means of this ensemble of models we are able to identify robust results concerning incentives of nations to commit themselves to a climate agreement, and to estimate what stable agreements can achieve in terms of greenhouse gas mitigation. We also assess the potential of transfers that redistribute the surplus of cooperation in order to foster stability of climate coalitions. In contrast to much of the existing analytical game theoretical literature, we find substantial scope for self-enforcing climate coalitions in most models that close much of the abatement and welfare gap between complete absence of cooperation and full cooperation. This more positive message follows from the use of transfer schemes that are designed to counteract free riding incentives.

The potential of geoengineering as an alternative or complementary option to mitigation and adaptation has received increased interest in recent years. The scientific assessment of geoengineering is driven to a large extent by assumptions about its effectiveness, costs, and impacts, all of which are highly uncertain. This has led to a polarizing debate. This paper evaluates the role of Solar Radiation Management (SRM) on the optimal abatement path, focusing on the uncertainty about the effectiveness of SRM and the interaction with uncertain climate change response. Using standard economic models of dynamic decision theory under uncertainty, we show that abatement is decreasing in the probability of success of SRM, but that this relation is concave and thus that significant abatement reductions are optimal only if SRM is very likely to be effective. The results are confirmed even when considering positive correlation structures between the effectiveness of geoengineering and the magnitude of climate change. Using a stochastic version of an Integrated Assessment Model, the results are found to be robust for a wide range of parameters specification.

The potential of geoengineering as an alternative or complementary option to mitigation and adaptation has received increased interest in recent years. The scientific assessment of geoengineering is driven to a large extent by assumptions about its effectiveness, costs, and impacts, all of which are highly uncertain. This has led to a polarizing debate. This paper evaluates the role of Solar Radiation Management (SRM) on the optimal abatement path, focusing on the uncertainty about the effectiveness of SRM and the interaction with uncertain climate change response. Using standard economic models of dynamic decision theory under uncertainty, we show that abatement is decreasing in the probability of success of SRM, but that this relation is concave and thus that significant abatement reductions are optimal only if SRM is very likely to be effective. The results are confirmed even when considering positive correlation structures between the effectiveness of geoengineering and the magnitude of climate change. Using a stochastic version of an Integrated Assessment Model, the results are found to be robust for a wide range of parameters specification.

"This report presents the results of a research project carried out as part of the postgraduate training course of the German Development Institute (GDI), Bonn, in close cooperation with the Centre for Social Markets, India, and with the support of Ashok V. Desai, Consultant Editor of the Telegraph, India. The report is based on studies of the literature and on empirical data collected in India’s main industrial districts of Delhi, Mumbai, Pune, Bangalore and Chennai from February to April 2006. The GDI research team would like to thank Sachin Joshi of the Centre for Social Markets and Ashok V. Desai for their strong scientific and technical support, without which this study would not have been possible. The research team would also like to express their thanks to the top officials of almost 40 Indian and foreign companies and 32 other stakeholders who spoke of their experience and responded to our questionnaire on CSR in India and the role of the UN Global Compact in particular. Last but not least, the authors are very grateful to experts from other Indian, international and German institutions who were kind enough to share their knowledge with us and so contributed to the writing of the report. However, the GDI team alone is responsible for the results presented here." (excerpt) Der vorliegende Beitrag untersucht die UN Global Compact-Initiative (UNGC), die im Jahr 2000 von dem damaligen UN-Generalsekretär Kofi Annan aus der Taufe gehoben wurde, unter besonderer Berücksichtigung der sozialen und ökologischen Aktivitäten in Indien. Der Global Compact, ein weltweiter Pakt, der zwischen Unternehmen und der UNO geschlossen wird, basiert auf zehn Prinzipien, die der Einhaltung bestimmter sozialer und ökologischer Mindeststandards dienen. Dies sind beispielsweise die wesentlichen Konventionen zur Einhaltung der Menschenrechte, Vereinigungsfreiheit, keine Zwangsarbeit, keine Kinderarbeit, Nicht-Diskriminierung, wesentliche Prinzipien, wie sie in den Rio-Konventionen zum Umweltschutz verfasst worden sind, sowie das Prinzip gegen alle Formen von Korruption. (ICD2)

We study the damped wave equation with time-dependent coefficients and prove energy estimates for a new class of coefficients.

Benefit-cost analyses of climate policies by integrated assessment models have generated conflicting assessments. Two critical issues affecting social welfare are regional heterogeneity and inequality. These have only partly been accounted for in existing frameworks. Here, we present a benefit-cost model with more than 50 regions, calibrated upon emissions and mitigation cost data from detailed-process IAMs, and featuring country-level economic damages. We compare countries’ self-interested and cooperative behaviour under a range of assumptions about socioeconomic development, climate impacts, and preferences over time and inequality. Results indicate that without international cooperation, global temperature rises, though less than in commonly-used reference scenarios. Cooperation stabilizes temperature within the Paris goals (1.80∘C [1.53∘C–2.31∘C] in 2100). Nevertheless, economic inequality persists: the ratio between top and bottom income deciles is 117% higher than without climate change impacts, even for economically optimal pathways.

Climate engineering, and in particular solar radiation management (SRM), is attracting increasing attention as a climate policy option. However, its potentially strategic nature and unforeseen side effects provide major policy and scientific challenges. We study the role of SRM in a two-country model with the notable feature of deep uncertainty modeled as model misspecification of SRM side effects. We find that deep uncertainty leads to a reduction in SRM deployment under both global cooperation and strategic Nash behavior, and that the effect is larger if countries act strategically. Furthermore, we demonstrate that if countries have different model confidence about SRM impacts, then the more confident country will engage more strongly in using SRM, leading this country to “free drive”.