Feed-in tariffs and the carbon emission trading scheme under China's peak emission target: A dynamic CGE analysis for the development of renewable electricity.

Thermal power industry is one of China's leading sources of carbon emissions. China has launched a national carbon emissions trading scheme (ETS) and renewable energy incentive programs to achieve its peak emission target by 2030. However, since 2021, China no longer provides a central feed-in tariff (FIT) for new centrally located solar photovoltaic (PV) power plants, commercial and industrially distributed PV projects, and newly approved onshore wind power projects. This change in policy may threaten China's carbon reduction targets and economic development. Using a dynamic computable general equilibrium (CGE) model, we assess the combined effects of carbon ETS and FIT on China's electricity sector, carbon emission peak target, renewable energy and economic development. In terms of policy overlap and integration, we analyze the impact of these policies and estimate how to coordinate FIT and carbon ETS policies to ensure their effectiveness. Results show that the overall effects of FIT subsidies are superior to phasing-out FIT scenarios. The fiscal pressure caused by FIT is lower than its actual expenditure because it stimulates economic activity and boosts government revenue. However, considering the multiplier effect of the FIT on promoting government revenue growth and GDP growth, the most effective FIT should be terminated in 2025, followed by subsidies ending in 2030 and 2035.

Impact of regional energy allocation distortion on carbon emission efficiency: Evidence from China.

The free flow of energy cannot be fully achieved in China's energy market because of incomplete market-oriented reform, resulting in energy allocation distortion, which has hampered carbon emissions reduction. However, the extent of energy allocation distortion and its role in carbon emission efficiency remain unexplored. Therefore, this study aims to measure energy allocation distortion and investigate its impact on carbon emission efficiency. For this purpose, first, we derive energy allocation distortion based on a production function and carbon emission efficiency using a meta-frontier non-radial Malmquist index. To effectively address the endogeneity issue, we use a generalized method of moments model to estimate the impact of energy allocation distortion on carbon emission efficiency. Second, we further explore the distortionary mechanism of carbon emission efficiency associated with energy allocation and analyze the asymmetric effect of energy allocation distortion on carbon emission efficiency. The results show a certain degree of energy allocation distortion throughout the country, and disparity exists among different regions. The average value of carbon emission efficiency in the eastern region is 1.0286, well ahead of the national average, demonstrating better performance than other regions. Energy allocation distortion negatively affects carbon emission efficiency, with a 1% increase in energy allocation distortion leading to a 0.251% decrease in carbon emission efficiency. Technological progress, the structure of energy consumption, and industrial structure are important transmission channels through which energy allocation affects carbon emission efficiency. The study contributes to uncovering regional energy allocation distortion and its impacts on carbon emission efficiency and providing strategic policy recommendations for improving energy allocation efficiency.

Climate co-benefits of VOC control policies in China based on a cross-scale approach.

Volatile organic compounds (VOC) contributing to smog formation, have been an important indicator of atmospheric governance during China's "14th Five-Year Plan". VOC would be possibly incorporated into the scope of environmental protection tax, but previous studies have seldom explored impacts of VOC control policies at national and regional levels. Here, we design a national uniform VOC control policy, as well as two regionally differentiated policies based on regional disparities in PM2.5 concentrations and energy intensity by using a cross-scale dynamic computable general equilibrium (CGE) model. Our analysis is to assess the impacts of these policies on VOC, CO2, sulfur dioxide (SO2), nitrogen oxides (NOX), and PM2.5 emissions, air quality and environmental equity, and to estimate health benefits, policy costs and net benefits. We find that national and regionally differentiated VOC control policies generally lead to VOC emission reductions and generate co-benefits on emission reductions in CO2, SO2, NOX and PM2.5 at national and provincial levels. However, regional emission leakage exists due to differences in the provincial costs of VOC mitigation. The regionally differentiated VOC pricing policies are found to be more effective to enhance environmental equity than the uniform policy. In particular, the regionally differentiated VOC control policy based on provincial energy efficiency is found to be superior to other policies in terms of improve air quality. Furthermore, the human health benefits associated with VOC pricing policies would partially offset policy costs at both the national and regional levels. Our results suggest that policymakers would pay attention to developing regions with low energy efficiency which have the great emission reduction potential. Advanced producing technology and further end-of-pipe control measures to reduce non-combustion PM2.5 emissions are needed. VOC policy designed based on provincial energy efficiency provides great insights for environmental policy making to accomplish 2035 goal of building a Beautiful China.

Environmental impact of national and subnational carbon policies in China based on a multi-regional dynamic CGE model.

Carbon policies are important not only for mitigating global climate change, but also for controlling local and regional air pollution. The large regional disparities in economic development and air pollution across China calls for regionally differentiated policies. Previous studies have not systematically investigated the environmental impacts of regional carbon policies in China covering different spatial scales. This study constructs a multi-regional dynamic computable general equilibrium (CGE) model covering 30 provinces of China to assess the impacts of national and subnational carbon polices on CO2 emissions and co-emitted air pollutants from 2020 to 2050. We consider one national carbon policy which aims to achieve China's 2030 national CO2 abatement target, as well as three regional policies with the same policy stringency as the national one but only applied to eastern China (EP), the Jiangsu-Shanghai-Zhejiang area (JSZP), and the Beijing-Tianjin-Hebei area (BTHP), respectively. We find that regional policies (EP, JSZP, and BTHP) are as effective in reducing CO2 emissions in their targeted regions as the national policy. However, they lead to an increase in CO2 emissions in untargeted regions (the so-called "emissions leakage"). The CO2 leakage rates, which depend on the policy spatial coverage, are 4%, 13%, and 65% for EP, JSZP, and BTHP, respectively, in 2050. Compared with CO2, changes in air pollutant emissions, including sulfur dioxide (SO2), nitrogen oxides (NOx), volatile organic compounds (VOC), and fine particulate matter (PM2.5), exhibit a similar pattern under all policy scenarios, but the magnitude of change is significantly smaller. Transportation, thermal power, and some energy intensive industries are the three largest contributors to CO2 and air pollutant emission reductions. Our results suggest that regional carbon policy is effective in reducing CO2 and air pollutant emissions in the targeted regions, and extending the spatial coverage or increasing policy stringency can largely inhibit emissions leakage.

Spatial Dependence and Determinants of Dairy Farmers' Adoption of Best Management Practices for Water Protection in New Zealand.

This paper analyses spatial dependence and determinants of the New Zealand dairy farmers' adoption of best management practices to protect water quality. A Bayesian spatial durbin probit model is used to survey data collected from farmers in the Waikato region of New Zealand. The results show that farmers located near each other exhibit similar choice behaviour, indicating the importance of farmer interactions in adoption decisions. The results also address that information acquisition is the most important determinant of farmers' adoption of best management practices. Financial problems are considered a significant barrier to adopting best management practices. Overall, the existence of distance decay effect and spatial dependence in farmers' adoption decisions highlights the importance of accounting for spatial effects in farmers' decision-making, which emerges as crucial to the formulation of sustainable agriculture policy.

Synergistic effect of carbon ETS and carbon tax under China's peak emission target: A dynamic CGE analysis.

Global warming resulting from greenhouse gas emissions poses threats to humankind and has become a worldwide issue. As the top CO2 emitter in the world, China has committed to achieving its carbon emission peak by no later than 2030; in this context, how to best use and apply carbon emission reduction policy is particularly critical. By constructing a dynamic computable general equilibrium (CGE) model, we first examine a pure ETS included only the electricity sector in 2021, and the eight sectors starting in 2022, considering a declining carbon intensity rate of 4.5% and a higher rate of 4.8%. With the carbon intensity rates of 4.3% and 4.5%, we further evaluate two-hybrid systems of the carbon tax and carbon ETS, where the carbon tax of 10 yuan per ton is the starting levied rate in 2022 and increases at 4 yuan per ton year by year. The results proved that hybrid emission reduction policy can help reach a carbon emissions peak before 2030 and do so at a lower economic cost compared to the effect of pure carbon ETS. Besides, the coordinated use of a carbon tax and a carbon ETS can promote optimization of energy consumption structures and accelerate the decline of energy intensity and carbon intensity; this can contribute to curbing the growth of total energy consumption and total carbon emissions.

The macro-economic and CO2 emissions impacts of COVID-19 and recovery policies in China.

The outbreak and ongoing evolution of the COVID-19 pandemic have dramatically impacted economic development and CO2 emissions. China is under simultaneous pressure to recover from the outbreak and meet its carbon reduction targets, and the government is endeavouring to stimulate economic recovery through fiscal and monetary policies. This paper uses a computable general equilibrium model to measure the impact on China's economic recovery and carbon emissions by incorporating the pandemic shock and related economic recovery policies of loan prime rate (LPR) and value-added tax (VAT) reduction. The study found that COVID-19 led to a simultaneous shock on China's supply and demand sides in which GDP dropped by 2.62% and carbon emissions fell by 2.53%, compared to the period prior to COVID-19. Although the LPR and VAT reduction effectively mitigated economic loss, the combined LPR and VAT reduction had a more substantial effect on boosting GDP than the single policies. The VAT cut expands production and was used to overcome supply-side shocks, while lowering LPR mitigates the damage of demand-side shocks. Compared to the VAT reduction policy, reduced LPR has smaller carbon emissions per unit of GDP output. Consequently, we recommend that the government concentrate on a combination of policies to navigate pandemic shocks, as the two economic stimulus policies are confirmed to complement one another in terms of strengths and shortcomings.

An input-output structural decomposition analysis of changes in China's renewable energy consumption.

Governments actively encourage renewable energy use to deal with climate change and achieve carbon emission reduction targets. It is crucial to find out the driving factors that affect the utilization of renewable energy. Therefore, based on China's 2010-2016 input-output table, this paper uses the input-output model and structural decomposition analysis (SDA) to analyze the driving factors of renewable energy changes in the production end, household end, and the aggregate economy. The results show that the changes in the consumption structure (F) is the most crucial factor for renewable energy use, followed by technology progress (T) and final demand per capita (V). Sector SEHW (supply of electric power, heat power, and water) and MCRP (manufacture of coke and refined petroleum products) are the two vital sectors to achieve China's energy transition of the production level. However, as for households, the proportion of renewable energy has been declining. Hence, the government should promote renewable energy use and achieve the green transition in production and household levels.