2016年-世界发展银行全球_Impacts_of_Carbon_Pricing_in_Reducing_the_Carbon_Intensity_of_Chinas_GDP_108页_2mb

Summary of "Impacts of Carbon Pricing in Reducing the Carbon Intensity of China's GDP"

Core Content

This study evaluates the economic and environmental impacts of implementing carbon pricing mechanisms in China to meet its Nationally Determined Contribution (NDC) targets under the Paris Agreement. Specifically, it examines the feasibility of achieving a 60-65% reduction in carbon dioxide (CO₂) intensity of GDP by 2030 compared to 2005 levels. The analysis uses a dynamic computable general equilibrium (CGE) model to simulate different carbon pricing strategies and their effects on GDP, energy use, CO₂ emissions, and economic growth.

Main Objectives

• To analyze the economic and environmental impacts of carbon pricing in China.
• To assess the efficiency of different carbon pricing trajectories in meeting the NDC targets.
• To evaluate the effects of revenue recycling methods on GDP growth and carbon intensity reduction.

Key Findings

1. Carbon Pricing Trajectories

• A carbon price that starts low and rises gradually is more efficient in achieving the NDC targets than a constant carbon price.
• To meet the 60% reduction target, the carbon surcharge on fossil fuels increases from 1.6 yuan/ton CO₂ in 2015 to 26 yuan/ton CO₂ in 2030.
• For the 65% reduction target, the carbon surcharge rises from 9.8 yuan/ton CO₂ in 2015 to 157 yuan/ton CO₂ in 2030.

2. Economic Impacts

• GDP reduction: With the 65% target, GDP is 0.74% lower in 2030 than the base case, while with the 60% target, it is 0.11% lower.
• Energy use reduction: Energy use decreases by 2.6% for the 60% target and 13% for the 65% target.
• CO₂ emissions reduction: CO₂ emissions fall by 3.3% and 16%, respectively, under the 60% and 65% carbon pricing targets.

3. Revenue Recycling Methods

• Cutting existing distortionary taxes (e.g., VAT and capital income tax) is more effective in minimizing the economic impact compared to lump-sum transfers to households.
• If carbon revenue is given as a lump-sum rebate, GDP is 1.2% lower in 2030 than the base case, which is worse than the 0.74% reduction when taxes are cut.

4. Policy Scenarios

• R1CUT: 60% reduction in CO₂ intensity, with rising carbon prices and tax cuts.
• R2CUT: 65% reduction in CO₂ intensity, with rising carbon prices and tax cuts.
• R2LUMP: Same 65% reduction as R2CUT, but with lump-sum transfers to households, resulting in a larger GDP impact.
• F2CUT: Flat carbon price to achieve the same CO₂ reductions as R2CUT, leading to less GDP impact than the rising price scenario.
• RRSUB: Rising renewable subsidies to reach the IEA's New Policies scenario, with carbon revenues used to fund these subsidies.

5. Base Case Scenario

• The base case assumes continued growth in GDP, energy use, and CO₂ emissions without carbon pricing.
• GDP is projected to grow at 6.4% during 2015-20 and 4.6% during 2020-30.
• Energy intensity is expected to fall by 3.4% annually from 2015-20, and 2.9% annually from 2020-30.
• CO₂ intensity is projected to decrease by 58.7% in 2030 relative to 2005 levels, slightly below the NDC target of 60-65%.
• Coal use declines from 4.1 billion tons in 2020 to 4.6 billion tons in 2030, while renewables (wind, solar, hydro) grow rapidly, especially in the early years.

6. Model Structure

• The model includes 33 sectors, household heterogeneity, and dynamic capital formation.
• The electricity sector is disaggregated into 9 sub-sectors, including coal, gas, nuclear, hydro, wind, solar, and carbon capture technologies.
• Prices and outputs are determined by the interaction of supply and demand in the model.
• Carbon pricing is implemented through surcharges on fossil fuel prices.

7. Policy Implications

• The recycling of carbon revenue through tax cuts is a more efficient method to reduce the economic burden of carbon pricing.
• The gradual increase in carbon prices is more effective in achieving the NDC targets than a constant price.
• Renewable energy development can be supported through carbon pricing revenue, but this leads to a larger GDP impact than tax cuts.

Conclusion

The study concludes that carbon pricing, particularly with a rising rate and revenue recycling through tax cuts, is a viable and efficient policy tool for China to meet its NDC targets. While a higher carbon price is necessary to achieve the 65% reduction goal, it comes with a smaller economic cost compared to lump-sum transfers. The integration of renewable energy through carbon pricing can also contribute to the reduction of carbon intensity, though it may affect GDP growth more significantly. The model provides a useful framework for analyzing the long-term economic and environmental consequences of carbon pricing in China.