清洁供暖与清洁空气:来自中国煤改气项目的证据

IF 5.2 1区 经济学 Q1 ECONOMICS 中国经济评论 Pub Date : 2024-04-28 DOI:10.1016/j.chieco.2024.102179
Haijian Wang, Shaojie Zhou, Penglong Zhang
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引用次数: 0

摘要

环境恶化,尤其是空气污染,是中国近几十年来普遍存在的问题。本文研究的不是针对从事生产活动的企业的传统监管政策,而是针对中国居民的清洁取暖政策的效果。我们将煤改气项目作为一个准自然实验,利用城市级的日空气质量数据集,采用差分法研究了在北京及其周边地区实施的清洁取暖政策对空气质量的影响。结果表明,清洁取暖政策显著降低了煤改气城市的空气污染,改善了经济福利,对北京周边城市的影响更大。我们没有发现任何证据表明政策效果随时间推移而递减。此外,我们还发现增加补贴可以进一步提高政策效果。我们还检验了这一政策的空间溢出效应,并计算了其地理边界。在改变能源结构的同时调节居民排放对改善空气质量至关重要。
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Clean heating and clean air: Evidence from the coal-to-gas program in China

Environmental degradation, especially air pollution, has been a prevalent problem in China in recent decades. Rather than focus on traditional regulatory policies aimed at enterprises engaged in production activities, this paper studies the effects of the clean heating policy targeting residents in China. Taking the coal-to-gas program as a quasi-natural experiment and using a city-level dataset of daily air quality, we study the effects of the clean heating policy implemented in Beijing and its surrounding areas on air quality with the difference-in-differences method. The results show that the clean heating policy significantly reduced air pollution in coal-to-gas cities, bringing improved economic welfare, with stronger effects in Beijing's neighboring cities. We find no evidence suggesting a decreasing policy effect over time. In addition, we find that increasing subsidies can further enhance the policy's effectiveness. We also test the spatial spillover effects of this policy and calculate its geographical boundary. Changing the energy structure in conjunction with regulating residential emissions is crucial to improving air quality.

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来源期刊
中国经济评论
中国经济评论 ECONOMICS-
CiteScore
10.60
自引率
4.40%
发文量
380
期刊介绍: The China Economic Review publishes original works of scholarship which add to the knowledge of the economy of China and to economies as a discipline. We seek, in particular, papers dealing with policy, performance and institutional change. Empirical papers normally use a formal model, a data set, and standard statistical techniques. Submissions are subjected to double-blind peer review.
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