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Air pollution is a major sustainability challenge – and future anthropogenic precursor and greenhouse gas (GHG) emissions will greatly affect human well-being. While mitigating climate change can reduce air pollution both directly and indirectly, distinct policy levers can affect these two interconnected sustainability issues across a wide range of scenarios. We help to assess such issues by presenting a public Tool for Air Pollution Scenarios (TAPS) that can flexibly assess pollutant emissions from a variety of climate and air quality actions, through the tool's coupling with socioeconomic modeling of climate change mitigation. In this study, we develop and implement TAPS with three components: recent global and fuel-specific anthropogenic emissions inventories, scenarios of emitting activities to 2100 from the MIT Economic Projection and Policy Analysis (EPPA) model, and emissions intensity trends based on recent scenario data from the Greenhouse Gas–Air Pollution Interactions and Synergies (GAINS) model. An initial application shows that in scenarios with less climate and pollution policy ambition, near-term air quality improvements from existing policies are eclipsed by long-term emissions increases – particularly from industrial processes that combine sharp production growth with less stringent pollution controls in developing regions. Additional climate actions would substantially reduce air pollutant emissions related to fossil fuel (such as sulfur and nitrogen oxides), while further pollution controls would lead to larger reductions for ammonia and organic carbon (OC). Future applications of TAPS could explore diverse regional and global policies that affect these emissions, using pollutant emissions results to drive global atmospheric chemical transport models to study the scenarios' health impacts.

We describe the factors researchers should consider in deciding when and how to use computational general equilibrium (CGE) models for environmental policy analysis instead of partial equilibrium or engineering models. Special attention is given to modeling the social costs and benefits of regulations and the role played by labor markets. CGE models excel at quantifying interactions across different sectors of the economy, factor-market outcomes, and the distributional consequences of policy, all using a comprehensive set of the resource constraints faced by agents. The ceteris paribus nature of these experiments allows a skilled modeler to develop a systematic understanding of the connection between model assumptions and policy outcomes. Using CGE models to address environmental policy questions involves challenges, including the representation of narrow and technology-specific regulatory designs, data and aggregation issues, and the development of methods to improve model transparency and validity.

Air quality and climate change are substantial and linked sustainability challenges, and there is a need for improved tools to assess the implications of addressing them in combination. High-fidelity chemistry-climate simulations can capture combined climate-air quality responses to policy change, but computational cost has prevented integration of accurate air quality impacts into integrated assessment models (IAMs) used to inform policy development. Instead, IAMs often use global- or regional-scale marginal response factors to calculate air quality impacts of climate scenarios. To bridge this gap, we develop a computationally-efficient approach to quantify air quality impacts of combined climate and air quality interventions, capable of capturing spatial heterogeneity and complex atmospheric chemistry. We fit individual response surfaces to high-fidelity model simulation output for 1,525 locations worldwide under a variety of perturbation scenarios. Our intermediate-fidelity approach captures known differences in atmospheric chemical regimes, and can estimate location-by-location air quality under different scenarios within milliseconds. It can also be straightforwardly implemented in IAMs, enabling researchers to rapidly estimate how air quality in different locations, and related equity-based metrics, will respond to large-scale changes in emissions policy.Using this approach we find that the sensitivity of air quality to climate change and air pollutant emissions reductions differs in sign and magnitude by region, suggesting that calculations of “co-benefits” of climate policy that do not account for the existence of simultaneous air quality interventions can lead to inaccurate conclusions. Although reductions in global mean temperature are effective in improving air quality in many locations and sometimes yield compounding benefits, we show that the air quality impact of climate policy depends on air quality precursor emissions stringency.Our approach can be extended to include results from higher-resolution modeling, but also to incorporate other interventions towards sustainable development which interact with climate action and have spatially-distributed equity dimensions.

Growing societal pressures, technological trends and government and industry actions are moving the world toward decarbonization and away from the “business-as-usual”. As such, the concept of a single/obvious “business as usual” or “reference” scenario is no longer relevant. Instead, there are multiple plausible futures that should be explored. We contribute one such scenario that carefully considers emission-reduction trends and actions that are likely in the future, absent globally coordinated mitigation effort. We explore the long-term implications for energy, emissions, and temperature outcomes if the world continues to address climate change in the way it has so far—through piecemeal actions and growing social and technological pressures. This Growing Pressures scenario results in a central scenario outcome of about 3 °C of surface temperature warming, which is higher than the “well below 2 °C” level aspired by the Paris Agreement, but lower than many widely used “no-policy” scenarios. Ongoing and growing pressures of change, the roots of which are clearly visible today, could deliver a plausible energy transition scenario to near zero emissions that plays out over the coming century. While a more aggressive transition is clearly required, this finding highlights the need to bring actions forward in time to achieve an improved outcome making use of clearly identifiable policies and technologies.

The shale gas boom in the U.S. has lowered the U.S. CO2 emissions in recent years mainly through substitution of gas for coal in power generation. Will the shale gas boom reduce the emissions in the long-run as well? To study this, we consider a counterfactual without the shale gas boom based on a general equilibrium modeling for the 2011 U.S. economy. To enhance the power sector modeling, the supply responses of coal-fired and gas-fired generations are calibrated to existing research. We find that if gas prices remain at 2007 levels in 2011, only a model setting that allows very little reduction in electricity demand, reflecting a short-run demand response, generates an increase in economy-wide emissions. For all other cases, the higher gas price under the counterfactual will have a dampening effect on economic activities and consequently lowers economy-wide emissions, even though the power sector emissions may increase due to the gas-to-coal switch. In other words, without any policy intervention, although the shale gas boom could reduce emissions in the short run, it may lead to higher emissions in the long run if the low gas prices persist. Our finding suggests that extrapolating the current decline in emissions due to the shale gas boom to the distant future could be misleading. Instead, if curbing emissions is the goal, rather than depending upon the cheap gas, policies and measures for cutting emissions remain imperative.

We provide a critical evaluation about how updating the input–output data of a computable generation equilibrium model can affect policy results, an assessment that is rarely done in existing liter...

Because the Russian economy relies heavily on exports of fossil fuels, the primary source of human-induced greenhouse gas (GHG) emissions, it may be adversely impacted by Paris Agreement-based clim...

Multi-region multi-sector energy-economic models are often used to analyze long-term scenarios of energy development, however, these models usually rely on a simplified representation of technological details in power generation. To strengthen this representation, we develop a method for modeling the economic competition between different advanced technologies in multi-region multi-sector dynamic energy-economic models based on a markup approach, which represents the measure of the cost of a technology relative to the price received for electricity generation. The markup includes capital costs, fixed and variable operating and maintenance (O&M) costs, fuel costs, and transmission and distribution (T&D) costs. For intermittent technologies, it also includes a backup requirement to make these technologies effectively dispatchable. For carbon capture and storage (CCS) technologies, it also includes the costs of CO2 capture, transportation and storage. We provide a standardized markup calculation for generation technologies for different regions of the world, including USA, China, India, EU, Japan and others. Then we analyze the sensitivity of the calculation to critical inputs, including capital costs, fuel costs, carbon prices and capacity factors. We provide a detailed calculation of the relative costs of the following technologies: new pulverized coal, new pulverized coal with CCS, natural gas combined cycle, natural gas with CCS, biomass-fueled plant, biomass with CCS, advanced nuclear, wind (for small and medium penetration levels), solar, wind with backup (for large penetration levels), co-firing of coal and biomass combined with CCS, and advanced CCS on natural gas. For illustration, we incorporate the markups into the MIT Economic Projection and Policy Analysis (EPPA) model, a global multi-sector multi-sector dynamic energy-economic model with a detailed representation of power generation technologies, and run several scenarios. Our analysis and results provide insight on the deployment of different low-carbon power generation technologies depending on assumptions about carbon policy stringency.

The establishment of an emissions trading scheme (ETS) in China creates the potential for a “least cost” solution for achieving the greenhouse gas (GHG) emissions reductions required for China to meet its Paris Agreement pledges. China has pledged to reduce CO 2 intensity by 60–65% in 2030 relative to 2005 and to stop the increase in absolute CO 2 emissions around 2030. In this series of studies, we enhance the MIT Economic Projection and Policy Analysis (EPPA) model to include the latest assessments of the costs of power generation technologies in China to evaluate the impacts of different potential ETS pathways on deployment of carbon capture and storage (CCS) technology. This paper reports the results from baseline scenarios where power generation prices are assumed to be homogeneous across the country for a given mode of generation. We find that there are different pathways where CCS might play an important role in reducing the emission intensity in China's electricity sector, especially for low carbon intensity targets consistent with the ultimate goals of the Paris Agreement. Uncertainty about the exact technology mix suggests that decision makers should be wary of picking winning technologies, and should instead seek to provide incentives for emission reductions. While it will be challenging to meet the CO 2 intensity target of 550 g/kWh for the electric power sector by 2020, multiple pathways exist for achieving lower targets over a longer timeframe. Our initial analysis shows that carbon prices of 35–40$/tCO 2 make CCS technologies on coal-based generation cost-competitive against other modes of generation and that carbon prices higher than 100$/tCO 2 favor a major expansion of CCS. The next step is to confirm these initial results with more detailed modeling that takes into account granularity across China's energy sector at the provincial level.

We consider the interplay of climate change impacts, global mitigation policies, and the economic interests of developing countries to 2050. Focusing on Malawi, Mozambique, and Zambia, we employ a structural approach to biophysical and economic modeling that incorporates climate uncertainty and allows for rigorous comparison of climate, biophysical, and economic outcomes across global mitigation regimes. We find that effective global mitigation policies generate two sources of benefit. First, less distorted climate outcomes result in typically more favorable and less variable economic outcomes. Second, successful global mitigation policies reduce global fossil fuel producer prices, relative to unconstrained emissions, providing a substantial terms of trade boost of structural fuel importers. Combined, these gains are on the order of or greater than estimates of mitigation costs. These results highlight the interests of most developing countries in effective global mitigation policies, even in the relatively near term, with much larger benefits post-2050.

This paper measures the economic impacts of Brazil's climate mitigation strategies contained in its Nationally Determined Contribution (NDC). To do so, we employ the computable general equilibrium MIT Economic Projection and Policy Analysis model and simulate alternative carbon pricing scenarios (sectoral versus economy-wide carbon markets), set to achieve the country's overall emissions targets announced under the Paris Agreement. The results show relatively cheap emissions reductions from land-use changes and agriculture in the short run: the cost of the Brazilian NDC is predicted to be only 0.7 per cent of GDP in 2030. Further efforts to reduce carbon emissions beyond 2030 would require policy changes, since all the potential emissions reductions from deforestation would be finished and the capacity to expand renewable energy sources would be constrained. In this case, an economy-wide carbon pricing system would help substantially to avoid higher compliance costs.

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.

Using the MIT Economic Projection and Policy Analysis (EPPA) model, we explore factors influencing carbon capture and storage (CCS) deployment in power generation and its role in mitigating carbon emissions. We find that in the 2∘C scenario with EPPA’s base-case technology cost and performance assumptions, CCS plays an important role in the second half of the century: by 2100 CCS is applied to almost 40% of world electricity production, with a third coming from coal with CCS and the other two-thirds from gas with CCS. Results on CCS deployment depend on the assumed fraction of carbon captured in CCS power plants, as emissions constraints get tighter and the carbon price rises. Adding options for higher capture fractions or offsetting uncaptured emissions leads to greater deployment of CCS than in the 2∘C base case. We provide a sensitivity analysis by making favorable assumptions for CCS, nuclear and renewables. We also explore regional differences in the deployment of CCS. We find that US and Europe mostly rely on gas CCS, whereas China relies on coal CCS and India pursues both options. We also assess how these projections align with assessment of CO2 storage potential, and find that storage potential is larger than storage demand at both global and regional scales. Ultimately, we find that under stringent mitigation scenarios, the power sector relies on a mix of technological options, and the conditions that favor a particular mix of technologies differ by region.

We present estimates of carbon budgets for different levels of surface air temperature (SAT) increase from multiple 400-member ensembles of simulations with the MIT Earth System Model of intermediate complexity (MESM). Ensembles were carried out using distributions of climate parameters affecting climate system response to external forcing obtained by comparison of historical simulations with available observations.First, to evaluate MESM performance, we ran two ensembles: one with MESM forced by 1% per year increase in CO2 concentrations (and with non-CO2 greenhouse gases (GHG) at pre-industrial level) and the other with GHG concentrations from the RCP 8.5 scenario. Distributions of climate characteristics describing model response to increasing CO2 concentrations (e.g. TRC and TCRE) as well as values of carbon budgets of exceeding different SAT levels agree well with published estimates.Then we ran a number of ensembles with MESM driven by emissions produced by the MIT Economic Projection and Policy Analysis (EPPA) Model. Our results show that under stringent mitigation policy concerning non-CO2 GHGs, the SAT increase can be kept below 2°C relative to pre-industrial with 66% probability through the end of the 21st century without negative CO2 emissions. The SAT increase can also be restricted to 1.5°C with 50% probability if such policy is implemented immediately. If GHG emissions follow the path implied by the Paris Agreement pledges through 2030, then it would require either an unrealistically sharp drop in non-CO2 GHG emissions or negative CO2 emissions to stay below 1.5°C. Keeping the temperature increase below a chosen value (1.5°C or 2°C) beyond 2100 will most likely require negative CO2 emissions in part due to difficulties in restricting agricultural methane emissions.Further analysis shows that temperature change during the 21st century is also significantly affected by the assumption concerning decrease of SO2 emissions from energy-intensive industries. Implementation of technologies resulting in reduction of those emissions will decrease the probability of SAT staying below a given limit by about 7-12%. This will affect the time when negative CO2 emissions will become necessary to prevent temperature increase.

Distributional impacts of environmental policies have become an increasingly important consideration in policymaking. To evaluate the distributional impacts of carbon pricing with different revenue recycling schemes for the USA, we integrate national economic model for the USA with household microdata that provides consumption patterns and other socio-economic characteristics for thousands of households. Using this combined model, we explore the distributional impacts and the possible trade-offs between equity and efficiency of different revenue recycling schemes. We find that the choice of revenue recycling scheme has a limited effect on efficiency of the policy, but significant distributional impacts. Our analysis indicates that policy makers can mitigate negative distributional impacts with positive synergies on efficiency. Xaquin Garcia-Muros acknowledges financial support from the European Union's H2020-MSCA-IF-2017 Actions No 796650 (CHANCE project).

In the 2015 Paris Agreement, Turkey pledged to reduce greenhouse gas (GHG) emissions by 21% by 2030 relative to business-as-usual (BAU). We expect that fulfilling this pledge will likely require a reduced reliance on fossil-based energy and additional investments in low-carbon energy sources. To fully assess these impacts, we develop a computable general equilibrium (CGE) model of the Turkish economy that combines macroeconomic representation of non-electric sectors with a detailed power sector representation. We analyze several scenarios to assess the impact of an emission trading scheme: one including the planned nuclear development and a renewable subsidy scheme (BAU), and another with no nuclear technology allowed (NoN). Our assessment shows that in 2030, without policy, primary energy will be mainly oil, natural gas and coal. Under an emission trading scheme, however, coal-fired power generation vanishes by 2030 in both BAU and NoN due to the high cost of carbon. With nuclear (BAU), GHG emissions are 3.1% lower than NoN due to the resulting energy mix, allowing for a lower carbon price ($50/tCO2 in BAU compared to $70/tCO2 in NoN). Our results suggest that fulfillment of Turkey's pledge may be possible at a modest economic cost of about 0.8–1% by 2030.

Efforts to estimate the physical and economic impacts of future climate change face substantial challenges. To enrich the currently popular approaches to impact analysis—which involve evaluation of a damage function or multi-model comparisons based on a limited number of standardized scenarios—we propose integrating a geospatially resolved physical representation of impacts into a coupled human-Earth system modeling framework. Large internationally coordinated exercises cannot easily respond to new policy targets and the implementation of standard scenarios across models, institutions and research communities can yield inconsistent estimates. Here, we argue for a shift toward the use of a self-consistent integrated modeling framework to assess climate impacts, and discuss ways the integrated assessment modeling community can move in this direction. We then demonstrate the capabilities of such a modeling framework by conducting a multi-sectoral assessment of climate impacts under a range of consistent and integrated economic and climate scenarios that are responsive to new policies and business expectations., Climate impact assessments usually rely on modeling approaches that are either comprehensive but inflexible and inefficient, or lacking sufficient detail. Here the authors describe a framework to assess multi-sectoral climate impacts and show its capability and timely responsiveness to new policies.

When comparing oil and gas projects - their relative attractiveness, robustness, and contribution to markets - various dollar per barrel benchmarks are quoted in the literature and in public debates. Among these benchmarks are a variety of breakeven points (also called breakeven costs or breakeven prices), widely used to predict producer responses to market conditions. These analyses have not proved reliable because (1) there has been no broadly accepted agreement on the definitions of breakeven points, (2) there are various breakeven points (and other benchmarks) each of which is applicable only at a certain stage of the development of a resource, and (3) each breakeven point is considerably more dynamic than many observers anticipated, changing over time in response to internal and external drivers. In this paper we propose standardized definitions of each breakeven point, showing which elements of field and well development are included in each. We clarify the purpose of each breakeven point and specify at which stage of the development cycle the use of each becomes appropriate. We discuss in general terms the geological, geographical, product quality, and exchange rate factors that affect breakeven points. We describe other factors that contribute to tight oil market dynamics, including factors that accelerate the growth and retard the decline of production; technological and legal influences on the behavior of market participants; and infrastructure, labor, and financial inelasticities. The role of tight oil in short-term and medium-term oil market stability is discussed. Finally, we explore the implications of a broader, more rigorous, and more consistent application of the breakeven point concept, taking into account the inelasticities that accompany it.

Carbon capture and storage (CCS) technology is an important option in the portfolio of emission mitigation solutions in scenarios that lead to deep reductions in greenhouse gas (GHG) emissions. We focus on CCS application in hard-to-abate sectors (cement industry, iron and steel, chemicals) and introduce industrial CCS options into the MIT Economic Projection and Policy Analysis (EPPA) model, a global multi-region multi-sector energy-economic model that provides a basis for the analysis of long-term energy deployment. We use the EPPA model to explore the potential for industrial CCS in different parts of the world, under the assumptions that CCS is the only mitigation option for deep GHG emission reductions in industry and that negative emission options are not available for other sectors of the economy. We evaluate CCS deployment in a scenario that limits the increase in average global surface temperature to 2 °C above preindustrial levels. When industrial CCS is not available, global costs of reaching the target are higher by 12% in 2075 and 71% in 2100 relative to the cost of achieving the policy with CCS. Overall, industrial CCS enables continued growth in the use of energy-intensive goods along with large reductions in global and sectoral emissions. We find that in scenarios with stringent climate policy, CCS in the industry sector is a key mitigation option, and our approach provides a path to projecting the deployment of industrial CCS across industries and regions.

This paper assesses the range of CO2 transport and storage costs and evaluates their impact on economy-wide modeling results of decarbonization pathways. Much analytic work has been dedicated to evaluating the cost and performance of various CO2 capture technologies, but less attention has been paid to evaluating the cost of CO2 transport and storage. Many integrated assessment modeling studies assume a combined cost for CO2 transport and storage that is uniform in all regions, commonly estimated at $10/tCO2. Realistically, the cost of CO2 transport and storage is not fixed at $10/tCO2 and varies across geographic, geologic, and institutional settings. We surveyed the literature to identify key sources of variability in transport and storage costs and developed a method to quantify and incorporate these elements into a cost range. We find that onshore pipeline transport and storage costs vary from $4 to 45/tCO2 depending on key sources of variability including transport distance, scale (i.e. quantity of CO2 transported and stored), monitoring assumptions, reservoir geology, and transport cost variability such as pipeline capital costs. Using the MIT Economic Projection and Policy Analysis (EPPA) model, we examined the impact of variability in transport and storage costs by applying a range of uniform costs in all regions in a future where global temperature rise is limited to 2°C. We then developed three modeling cases where transport and storage costs vary regionally. In these latter cases, global cumulative CO2 captured and stored through 2100 ranges from 290 to 377 Gt CO2, compared to 425 Gt CO2 when costs are assumed to be uniformly $10/t CO2 in all regions. We conclude that the widely used assumption of $10/tCO2 for the transport and storage of CO2 is reasonable in some regions, but not in others. Moreover, CCS deployment is more sensitive to transport and storage costs in some regions than others, particularly China. More analysis is needed to further quantify CO2 transport and storage costs at a regional level.

Assessments of the geologic storage capacity of carbon dioxide in the current literature are incomplete and inconsistent, complicating efforts to assess the worldwide potential for carbon dioxide capture and storage (CCS). We developed a method for generating first-order estimates of storage capacity requiring minimal data to characterize a geologic formation. We show this simplified method accounts for the majority of the variance in storage capacity found in more detailed studies conducted in the United States. We apply our method to create a worldwide database of storage capacity, disaggregated into 18 regions, and compare this storage capacity to CCS deployment in the MIT Economic Prediction and Policy Analysis (EPPA) model. Globally, we estimate there are between 8,000 and 55,000 gigatonnes (Gt) of practically accessible geologic storage capacity for carbon dioxide. For most of the regions, our results indicate storage capacity is not a limiting factor for CCS deployment through the rest of this century even if stringent emissions reductions are required.

This paper explores the viability of a gas-to-liquids (GTL) technology and examines how GTL penetration could shape the evolution of the crude oil–natural gas price ratio. Much research has established the cointegrated relationship between crude oil and natural gas prices in the U.S. The persistently low U.S. natural gas prices in recent years seem to mark a shift in this relationship, and have led some in industry to begin considering investments in GTL capacity in the US. In order to look forward over decades when the underlying economic drivers may be outside of historical experience, we use a computable general equilibrium model of the global economy to evaluate the economic viability of GTL and its impact on the evolution of the crude oil–natural gas price ratio. Our results are negative for the potential role of GTL. In order to produce any meaningful penetration of GTL, we find it necessary to evaluate scenarios that seem extreme. With any carbon cap GTL is not viable. Moreover, even without a carbon cap of any kind, extremely optimistic assumptions about (i) the cost and efficiency of GTL technology and about (ii) the available resource base of natural gas and the cost of extraction, before the technology penetrates and it impacts the evolution of the crude oil–natural gas price ratio.

Carbon capture and storage (CCS) technology is an important option in the portfolio of emission mitigation technologies in scenarios that lead to deep reductions in greenhouse gas (GHG) emissions consistent with limiting increases in global average surface air temperature to 2 degrees Celsius (2C). Industrial CCS applications are more challenging to analyze than CCS in the power sector -- mainly due to the vast heterogeneity in industrial and fuel processes. Our study focuses on the cement industry and provides the estimated costs associated with several CCS options: coal-fired post-combustion capture (PCC), natural gas-fired PCC, and Cryogenic Carbon Capture (CCC). We explore regional cost estimates with variations in costs of capital and fuels to provide a basis for regional and global projections of industrial CCS deployment. We offer a methodology for incorporating the CCS cost information into energy-economic and integrated assessment models. Our methodology can be applied to other applications of CCS in the industrial sector. We illustrate our method by introducing the industrial CCS options into the MIT EPPA model, a global energy-economic model that provides a basis for the analysis of long-term energy deployment, and we discuss different scenarios for industrial CCS deployment in different parts of the world. We tested in the EPPA model the potential for industrial CCS under the assumptions that CCS is the only mitigation option for deep GHG emission reduction in industry and that negative emission options are not available for other sectors of the economy. Overall, industrial CCS enables the continued use of energy-intensive goods with large reductions in global and sectoral emissions. We find that in scenarios with stringent climate policy, CCS in the industry sector is a key mitigation option, and our approach provides a path to projecting the deployment of industrial CCS across industries and regions.

The European Union (EU) recently adopted CO2 emissions mandates for new passenger cars, requiring steady reductions to 95 gCO2/km in 2021. We use a multi-sector computable general equilibrium (CGE) model, which includes a private transportation sector with an empirically-based parameterization of the relationship between income growth and demand for vehicle miles traveled. The model also includes representation of fleet turnover, and opportunities for fuel use and emissions abatement, including representation of electric vehicles. We analyze the impact of the mandates on oil demand, CO2 emissions, and economic welfare, and compare the results to an emission trading scenario that achieves identical emissions reductions. We find that vehicle emission standards reduce CO2 emissions from transportation by about 50 MtCO2 and lower the oil expenditures by about €6 billion, but at a net added cost of €12 billion in 2020. Tightening CO2 standards further after 2021 would cost the EU economy an additional €24–63 billion in 2025, compared with an emission trading system that achieves the same economy-wide CO2 reduction. We offer a discussion of the design features for incorporating transport into the emission trading system.

A recent UN climate agreement has the potential to shift global energy consumption from a mix dominated by fossil fuels to one driven by low-carbon technologies. It is clear that if this happens, f...

Recent multilateral climate negotiations have underlined the importance of international cooperation and the need for support from developed to developing countries to address climate change. This raises the question of whether carbon market linkages could be used as a cooperation mechanism. Policy discussions surrounding such linkages have indicated that, should they operate, a limit would be set on the amount of carbon permits that could be imported by developed regions from developing countries. This paper analyzes the impact of limited carbon trading between an ETS in the EU or the US and a carbon market covering Chinese electricity and energy intensive sectors using a global economy-wide model. We find that the limit results in different carbon prices between China and Europe or the US. Although the impact on low-carbon technologies in China is moderate, global emission reductions are deeper than in the absence of international trading due to reduced carbon leakage. If China captures the rents associated with limited permit trading, we show that it is possible to find a limit threshold that makes both regions better off relative to carbon markets operating in isolation.

This paper provides an overview of the base-year assumptions and baseline projections for the set of models participating in the LAMP and CLIMACAP projects. We present the range in baseline projections for Latin America, and identify key differences between model projections including how these projections compare to historic trends. We find relatively large differences across models in base year assumptions related to population, GDP, energy and CO2 emissions due to the use of different data sources, but also conclude that this does not influence the range of projections. We find that population and GDP projections across models span a broad range, comparable to the range represented by the set of Shared Socioeconomic Pathways (SSPs). Kaya-factor decomposition indicates that the set of baseline scenarios mirrors trends experienced over the past decades. Emissions in Latin America are projected to rise as a result of GDP and population growth and a minor shift in the energy mix toward fossil fuels. Most scenarios assume a somewhat higher GDP growth than historically observed and continued decline of population growth. Minor changes in energy intensity or energy mix are projected over the next few decades.

Based on an in-depth analysis of results from the MIT Economic Projection and Policy Analysis (EPPA) model of climate policies for Brazil and Mexico, we demonstrate that commitments by Mexico and Brazil for 2020—made during the UN climate meetings in Copenhagen and Cancun—are reachable, but they come at different costs for each country. We find that Brazil's commitments will be met through reduced deforestation, and at no additional cost; however, Mexico's pledges will cost 4 billion US dollars in terms of reduced GDP in 2020. We explore short- and long-term implications of several policy scenarios after 2020, considering current policy debates in both countries. The comparative analysis of these two economies underscores the need for climate policy designed for the specific characteristics of each country, accounting for variables such as natural resources and current economic structure. Our results also suggest that both Brazil and Mexico may face other environmental and economic impacts from stringent global climate policies, affecting variables such as the value of energy resources in international trade.

A growing concern for using large scale applied general equilibrium models to analyze energy and environmental policies has been whether these models produce reliable projections. Based on the latest MIT Economic Projection and Policy Analysis model we developed, this study aims to tackle this question in several ways, including enriching the representation of consumer preferences to generate changes in consumption pattern consistent to those observed in different stages of economic development, comparing results of historical simulations against actual data, and conducting sensitivity analyses of future projections to key parameters under various policy scenarios. We find that: 1) as the economies grow, the empirically observed income elasticities of demand are better represented by our setting than by a pure Stone–Geary approach, 2) historical simulations in general perform better in developed regions than in developing regions, and 3) simulation results are more sensitive to GDP growth than energy and non-energy substitution elasticities and autonomous energy efficiency improvement.

Electric vehicles (EV) offer a solution for decarbonization of transport. We evaluate global scenarios of EV deployment and their impacts on total global CO2 emissions. For this assessment, we enhance the MIT Economic Projection and Policy Analysis (EPPA) model to represent the fleet dynamics of light-duty vehicles (LDV), including internal combustion engine (ICE) vehicles and EVs. For EV fleet, both plug-in hybrid vehicles (PHEV) and battery electric vehicles (BEV) are considered. We consider several illustrative scenarios and find that global LDV stock is projected to grow from 1.1 billion vehicles in 2015 to 1.65–1.75 billion in 2050, while global EV stock is growing from about a million in 2015 to about 585–825 million in 2050. At this level of market penetration, EVs would constitute one-third to one-half of the overall LDV fleet by 2050 in different scenarios, with the stricter carbon constraints implied in the Paris to 2 °C scenario leading to the largest EV share. Our modeling suggests that EV uptake will vary across regions. China, the U.S., and Europe remain the largest markets in our study timeframe, but the EV presence is projected to grow in all regions. While the global LDV fleet grows by about 50% by 2050, the corresponding CO2 emissions from LDV are reduced by about 50% in 2050 relative to 2015. Global carbon intensity of LDVs are reduced by about 70% from 2015 to 2050.