能源消费、结构变化与空气质量——基于省际面板数据的实证检验

doi:10.11821/dlyj020210527

摘要/Abstract

摘要：

资源和环境约束对我国能源消费提出了严峻挑战。雾霾在一定程度上可以看做是终端能源消费结构性矛盾长期积累的集中爆发。本文借助K-means聚类、空间相关分析等方法,对30个省级单元的终端能源消费量的时空特征及其结构演变进行了系统研究。进一步,运用空间计量模型多角度探讨终端能源消费对环境污染的影响。研究发现：① 从人均终端能源消费量来看,在研究时段内各省级单元均有不同程度提升,但省际差异依然显著。② 从终端能源消费量来看,在研究时段内具有显著的空间正相关性,表明了空间集聚特征的存在,并形成了多种空间集聚类型。③ 从终端能源消费结构来看,由于资源禀赋、产业结构等方面的较大差异,不同省级单元的各类型能源消费结构均呈现出多样化演变特征。④ 通过空间计量模型检验发现,终端能源消费对空气质量指数有显著正向影响,并且,这种影响在不同能源消费结构与不同区域有明显差异性。⑤ 根据研究结论,有针对性地提出能源消费与结构优化的对策建议,以期从能源消费这一视角为减少碳排放、改善空气质量提供思路和参考。

关键词: 能源消费, 结构变化, 环境污染, 中国

Abstract:

Resource and environmental constraints pose severe challenges to China's energy consumption. Smog can be seen as a concentrated outbreak of long-term accumulation of structural contradictions in terminal energy consumption. With the help of K-means clustering, spatial correlation analysis and other methods, this paper systematically studies the spatiotemporal characteristics and structural evolution of the terminal energy consumption of 30 provincial-level units. Furthermore, the spatial measurement model is used to explore the impact of terminal energy consumption on environmental pollution from multiple perspectives. The results show that: (1) Per capita terminal energy consumption of each provincial-level unit has increased to varying degrees during the study period, but the inter-provincial differences are still significant. (2) In terms of terminal energy consumption, during the study period there is a significant positive spatial correlation, indicating the existence of spatial agglomeration characteristics, and the formation of a variety of spatial agglomeration types. (3) As for terminal energy consumption structure, due to large differences in resource endowments and industrial structures, various types in different provincial-level units show diversified evolutionary characteristics. (4) Through spatial econometric model testing, it is found that terminal energy consumption has a significant positive impact on the air quality index, and this impact differs in different energy consumption types. There are obvious differences between the structure and different regions. (5) Based on the research conclusions, targeted countermeasures and suggestions for energy consumption and structural optimization are put forward, in order to provide ideas and references for reducing carbon emissions and improving air quality. Finally, this article attempts to explain the impact of energy on air pollution from the consumption side of energy, but the production side of energy and the intermediate stage of processing and conversion may also form a more complex path of impact on environmental pollution, which is worthy of further analysis. In addition, with the continuous advancement of environmental protection technology, China's clean energy continues to increase, gradually transforming from auxiliary energy to main energy, and a clean, low-carbon, safe and efficient energy system is being built. Therefore, the complex relationship between clean energy production, consumption, resource allocation, technological innovation, and market-oriented reforms, and air quality will become the focus of subsequent exploration.

Key words: energy consumption, structural changes, environmental pollution, China

庄汝龙, 宓科娜. 能源消费、结构变化与空气质量——基于省际面板数据的实证检验[J]. 地理研究, 2022, 41(1): 210-228.

ZHUANG Rulong, MI Kena. Energy consumption, structural changes and air quality:Empirical test based on inter-provincial panel data[J]. GEOGRAPHICAL RESEARCH, 2022, 41(1): 210-228.

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变量	样本（个）	平均值	标准差	最小值	最大值
aqi	210	88.827	23.964	39.417	196.106
zdxfl	210	9252.715	4976.199	1461.461	25822.230
ecbz	210	0.423	0.082	0.162	0.568
wstzbz	210	0.055	0.055	0.008	0.271
czhl	210	58.731	11.631	37.830	89.600
rjldmj	210	20.655	11.257	7.447	64.986
rjkjzc	210	318.939	363.104	61.279	2012.163

年份	全局Moran′s I	预期指数	方差	Z得分	P值
2013	0.285822	-0.034483	0.005554	4.298105	0.000017
2014	0.489033	-0.034483	0.005718	6.923119	0.000000
2015	0.439769	-0.034483	0.005846	6.202518	0.000000
2016	0.489197	-0.034483	0.005851	6.846078	0.000000
2017	0.456709	-0.034483	0.005809	6.444467	0.000000
2018	0.440898	-0.034483	0.005850	6.215061	0.000000
2019	0.431691	-0.034483	0.005882	6.078568	0.000000

检验方法	统计值	P值
Hausman	33.060	0.0000
LM-Spatial error	138.739	0.0000
Robust LM-Spatial error	3.900	0.0480
LM-Spatial lag	138.774	0.0000
Robust LM-Spatial lag	3.935	0.0470
LR-Spatial error	15.900	0.0143
LR-Spatial lag	15.860	0.0146
LR-SDM-ind	31.430	0.0005
LR-SDM-time	344.840	0.0000

	OLS	SDM-IW	SDM-IW1	SDM-IW2	SDM-IW3
ln zdxfl	0.270**	0.272***	0.312***	0.181**	0.217**
	(0.114)	(0.086)	(0.090)	(0.087)	(0.086)
ecbz	1.333***	0.620**	0.542*	0.772***	0.556**
	(0.265)	(0.258)	(0.294)	(0.261)	(0.263)
wstzbz	-0.159	0.927***	0.893***	1.012***	0.850***
	(0.345)	(0.278)	(0.294)	(0.275)	(0.279)
ln czhl	-1.128***	-0.456*	-0.828***	-0.714***	-0.726***
	(0.282)	(0.258)	(0.248)	(0.267)	(0.231)
ln rjldmj	0.011	0.132**	0.171**	0.080	0.099
	(0.081)	(0.060)	(0.070)	(0.063)	(0.061)
ln rjkjzc	-0.026	-0.046*	-0.007	-0.037	-0.025
	(0.034)	(0.027)	(0.028)	(0.027)	(0.028)
W×ln zdxfl		0.312	1.535*	0.109	0.021
		(0.229)	(0.870)	(0.140)	(0.176)
W×Y		0.259***	-0.006	0.210***	0.264***
		(0.085)	(0.260)	(0.073)	(0.085)
W×X′		Yes	Yes	Yes	Yes
个体固定	Yes	Yes	Yes	Yes	Yes
时间固定	Yes	Yes	Yes	Yes	Yes
N（个）	210	210	210	210	210
r²_a	0.4520	0.0232	0.0118	0.1158	0.0677

	ln aqi	ln aqi	ln aqi
ln zdxfl_east	0.417*
	(0.251)
ln zdxfl_ns		0.190**
		(0.089)
ln zdxfl_hhy			0.216*
			(0.127)
X′	Yes	Yes	Yes
W×X′	Yes	Yes	Yes
W×Y	Yes	Yes	Yes
个体固定	Yes	Yes	Yes
时间固定	Yes	Yes	Yes
N（个）	210	210	210
r²_a	0.0107	0.3519	0.0003