Taiwan has proposed significant reductions in its greenhouse gas (GHG) emissions in its nationally determined contribution (NDC) to the Paris Agreement on climate change: a 50 percent cut from the business-as-usual level by 2030. Evaluating the impact of such climate mitigation policy on Taiwan is no easy task because its economy depends heavily on international trade, including imports of fossil fuels that account for nearly all of its energy supply. To date, studies assessing the economic impact of emissions reduction policies on Taiwan’s economy have been conducted solely under a single-country modeling framework, which cannot capture global effects such as impacts of climate mitigation policies abroad. To bridge this gap, researchers from the MIT Joint Program on the Science and Policy of Global Change and Taiwan’s Institute for Nuclear Energy Research developed a version of the MIT Economic Projection and Policy Analysis (EPPA) model, a global energy-economic computable general equilibrium (CGE) model, in which Taiwan is explicitly represented.

The new Economic Projection and Policy Analysis (EPPA)-Taiwan model has enabled the researchers to assess (1) how different reference-year data sets affect results of policy simulations, (2) the importance of structural and parameter assumptions in the model, and (3) the importance of explicit treatment of trade and international policy. Using the model, they found (1) higher mitigation costs across regions using data for the year 2011 rather than for 2007 and 2004 data, due to increasing fossil fuel cost shares over time; (2) lower GDP losses across regions under a broad carbon policy using a more complex model structure designed to identify the role of energy and GHG emissions in the economy, because the formulation allows more substitution possibilities than a much simplified production structure; and (3) lower negative impacts on GDP in Taiwan when it carries out its NDC as part of a global policy compared with unilateral implementation because, under a global policy, producer prices for fossil fuels are suppressed, benefiting Taiwan’s economy.

Answering these questions may help researchers and policymakers to become aware of the potential implications of updating the global economic database, demonstrate the impact of model design on results, and highlight the roles of policies implemented abroad in determining the domestic policy implications of Taiwan. Through their evaluation of the first stage of development of the EPPA-Taiwan model, the researchers have identified many additional steps to make the model more realistic.

Abstract: We present and evaluate a new global computable general equilibrium (CGE) model to focus on analyzing climate policy implications for Taiwan’s economy and its relationship to important trading partners. The main focus of the paper is a critical evaluation of data and model structure. Specifically, we evaluate the following questions: How do the different reference year data sets affect results of policy simulations? How important are structural and parameter assumptions? Are explicit treatment of trade and international policy important? We find: (1) Higher mitigation costs across regions using data for the year of 2011, as opposed to cases using the 2007 and 2004 data, due to increasing energy cost shares over time. (2) Lower GDP losses across regions under a broad carbon policy using a more complex model structure designed to identify the role of energy and GHG emissions in the economy, because the formulation allows more substitution possibilities than a more simplified production structure. (3) Lower negative impacts on GDP in Taiwan when it carries out its national determined contribution (NDC) as part of a global policy compared with unilateral implementation because, under a global policy, producer prices for fossil fuels are suppressed, benefitting Taiwan’s economy.

The impact of environmental regulation on firm productivity has been long been debated, however, mainly for western economies and with limited firm-level evidence. We study the impact of a large-scale national energy saving program (the Top 1000 Energy-Consuming Enterprises Program, or T1000P, 2006-2010) in China on firm productivity in the iron and steel industry. The T1000P assigned targets for reducing the energy consumption of approximately 1000 most energy-consuming industrial firms. Using detailed data from the China Industrial Census on 5,340 firms for the period of 2003 to 2008, we estimate a positive effect of the T1000P on firms in the iron and steel industry. Specifically, we find T1000P firms are associated with significantly greater annualized TFP change (an increase of 3.1 percent on average), suggesting the competitiveness of treated firms increased. Effects on technical change and scale efficiency change are positive and statistically significant, and contribute about equally to the overall treatment effect. Results are robust to instrumenting for policy exposure and other alternative specifications. Private benefits to firms from the policy likely reflect the combination of incentives and targets applied under the program.

The influence of anthropogenic aerosol emissions on the optical properties of clouds and the radiative forcing arising from these interactions, known as the aerosol indirect effect on climate, constitutes a fundamental uncertainty in our understanding of 20^th century climate change. In this dissertation, we investigate the role of a keystone physical process, droplet activation, in contributing to this uncertainty. The first half of the ensuing work focuses on the parameterization of this process in global model, assessing both existing schemes and developing a novel one. The second half then quantifies the influence of activation by using a suite of aerosol-climate models which include a complete description of the physics which give rise to the indirect effect.

Parameterizations of droplet activation perform well for idealized single-mode aerosol populations, but show systematic biases in high-pollution, weak-updraft regimes. These are exacerbated when the aerosol in question is a complex mixture. We show that estimates of droplet nucleation are highly sensitive to changes in the accumulation mode size and number concentration; this mode is itself sensitive to anthropogenic aerosol emissions, which potentially further biases modeled cloud droplet number. Using a model emulation technique, we develop a framework for building efficient metamodels of activation, which greatly reduce the mean error in droplet number predicted across regimes.

The biases in these parameterizations raise questions the influence of activation on the indirect effect. Using different schemes, we calculate a spread of 1Wm⁻² in the indirect effect, which we show is equal to the spread computed from an independent suite of global models with different aerosol and physics modules. The estimated indirect effect scales more strongly with the baseline cloud droplet number concentration simulated by each model than by its change from pre-industrial to present day, indicating a strong saturation effect. While present-day estimates of aerosol-cloud interactions derived from satellite-based instruments are inadequate at constraining the pre-industrial cloud droplet burden, we show that process-based measurements could overcome this problem.