基于投资视角的居民消费结构变动对环境效率的影响

doi:10.18402/resci.2021.09.05

摘要/Abstract

摘要：

居民消费结构和投资结构良性互动是畅通国民经济循环、实现经济高质量发展和生态环境高水平保护的重要路径。本文基于投资视角,从理论角度阐释居民消费结构变动对环境效率的影响机制,并利用2003—2017年中国省际面板数据,使用系统GMM模型实证分析了居民消费结构升级如何通过投资结构变化影响环境效率。研究表明：①在影响机制上,受环境规制、投资回报、清洁技术等因素影响,居民消费结构变动伴生的消费偏好改变将引起投资结构变动,并进一步作用于环境效率;②居民消费结构从生存型向发展型升级不仅对环境效率会产生直接正向影响,还会通过投资结构变动间接影响环境效率,但间接影响效应的大小和方向取决于投资结构偏向;③居民消费结构升级有利于推动投资结构向第三产业优化,进而正向促进环境效率提高。但投资结构若偏向高污染行业,虽能提高产出水平,但环境效率反而会因环境污染出现下降。因此,加快消费结构绿色转型、引导投资向发展型消费倾斜、加大顺应消费结构升级的低排放、气候适应型投资是实现经济与环境可持续发展的可行选择。

关键词: 消费结构, 投资结构, 环境效率, 非期望产出, SBM模型, 交互效应

Abstract:

A positive interaction between household consumption structure and investment structure is crucial to achieving a balance between economic development and environmental protection. This article presented a theoretical and empirical analysis of the impact of household consumption structure change on environmental efficiency from the perspective of investment. Using panel data on 30 provinces in China over the period 2003 to 2017, we examined the effects of upgrading the household consumption structure on environmental efficiency. The results indicate that: (1) Due to environmental regulations, investment returns, and clean technology, shifts in consumer preferences associated with consumption structure will change investment structure and further affect environmental efficiency. (2) The upgrading of the consumption structure from a survival to a development orientation positively improved environmental efficiency not only directly but also indirectly through the intermediary effect of investment structure change.However, the magnitude and direction of the indirect effect depend on the preference of investment structure. (3) The shift to investment in tertiary industries caused by upgrading the consumption structure effectively improved environmental efficiency. However, if the investment shift is biased toward high-polluting industries, then environmental efficiency is reduced due to increased environmental pollution. Therefore, accelerating the green transformation of consumption, guiding investment toward development-oriented consumption, and increasing low-emissions and climate-adaptive investments will help to achieve sustainable economic and environmental development.

Key words: consumption structure, investment structure, environmental efficiency, undesired output, SBM model, interaction effect

赵佳, 朱雨可. 基于投资视角的居民消费结构变动对环境效率的影响[J]. 资源科学, 2021, 43(9): 1764-1777.

ZHAO Jia, ZHU Yuke. Impact of consumption structure change on environmental efficiency from the perspective of investment[J]. Resources Science, 2021, 43(9): 1764-1777.

图/表 6

图1

表1

表2

表3

表4

表5

参考文献 34

[1]	柯美高. 贸易自由化对中国消费污染的影响及机制研究[J]. 中南财经政法大学学报, 2020, (5):125-136.
	[ Ke M G. The effect and mechanism of trade liberalization on consumption pollution in China[J]. Journal of Zhongnan University of Economics and Law, 2020, (5):125-136.]
[2]	关利欣. 以消费为引领构建新发展格局[N]. 经济参考报, 2020-10-15(1).
	[ Guan L X. Building a New Development Pattern Guided by Consumption[N]. Economic Information Daily, 2020-10-15(1).]
[3]	Jia J S, Gong Z H, Chen C D, et al. Incorporating carbon emissions from landfills and wastewater treatment into a household emission inventory for systematically analysing household behaviour[J]. Journal of Water and Climate Change, 2019, 10(4):708-724. doi: 10.2166/wcc.2018.071
[4]	Yang L, Wang Y T, Wang R R, et al. Environmental-social-economic footprints of consumption and trade in the Asia-Pacific region[J]. Nature Communications, 2020, DOI: 10.1038/s41467-020-18338-3. doi: 10.1038/s41467-020-18338-3
[5]	Hawkins J, Ma C, Schilizzi S, et al. China’s changing diet and its impacts on greenhouse gas emissions: An index decomposition analysis[J]. Australian Journal of Agricultural & Resource Economics, 2018, 62(1):45-64.
[6]	Gwozdz W, Nielsen K S, Müller T. An environmental perspective on clothing consumption: Consumer segments and their behavioral patterns[J]. Sustainability, 2017, 9(5):762-788. doi: 10.3390/su9050762
[7]	张雪峰, 宋鸽, 闫勇. 城市低碳交通体系对能源消费结构的影响研究: 来自中国十四个城市的面板数据经验[J]. 中国管理科学, 2020, 28(12):173-183.
	[ Zhang X F, Song G, Yan Y. The impact of urban low-carbon transportation system on the improvement of the structure of energy consumption: Evidence from 14 cities in China[J]. Chinese Journal of Management Science, 2020, 28(12):173-183.]
[8]	Zhang M, Ding S T, Pang J G, et al. The effect of indirect household energy consumption on PM 2.5 emission in China: An analysis based on CLA method[J]. Journal of Environmental Management, 2020, DOI: 10.1016/j.jenvman.2020.111531. doi: 10.1016/j.jenvman.2020.111531
[9]	Martinez S, Delgado M D M, Marin R M, et al. Identifying the environmental footprint by source of supply chains for effective policy making: The case of Spanish households consumption[J]. Environmental Science and Pollution Research, 2019, 26(32):33451-33465. doi: 10.1007/s11356-019-06296-3
[10]	毛克贞, 孙菁靖, 宋长健. 城镇居民消费增长加剧了生活污染吗?[J]. 华东经济管理, 2018, 32(4):87-95.
	[ Mao K Z, Sun J J, Song C J. Dose the growth of urban residents consumption exacerbate domestic pollution?[J]. East China Economic Management, 2018, 32(4):87-95.]
[11]	罗能生, 张梦迪. 人口规模、消费结构和环境效率[J]. 人口研究, 2017, 41(3):38-52.
	[ Luo N S, Zhang M D. Population size, consumption structure and environmental efficiency[J]. Population Research, 2017, 41(3):38-52.]
[12]	俞剑, 方福前. 中国城乡居民消费结构升级对经济增长的影响[J]. 中国人民大学学报, 2015, 29(5):68-78.
	[ Yu J, Fang F Q. The impacts of urban and rural household consumption upgrade on economic growth in China[J]. Journal of Renmin University of China, 2015, 29(5):68-78.]
[13]	Perez-Montiel J, Erbina C M. Investment sustained by consumption: A linear and nonlinear time series analysis[J]. Sustainability, 2020, 12(11):4430-4430. doi: 10.3390/su12114430
[14]	干春晖, 余典范, 余红心. 市场调节、结构失衡与产业结构升级[J]. 当代经济科学, 2020, 42(1):98-107.
	[ Gan C H, Yu D F, Yu H X. Market regulation, structural imbalance and industrial structure upgrading[J]. Modern Economic Science, 2020, 42(1):98-107.]
[15]	Xu Q, Lei Y L, Ge J P, et al. Did investment become green in China? Evidence from a sectoral panel analysis from 2003 to 2012[J]. Journal of Cleaner Production, 2017, 156:500-506. doi: 10.1016/j.jclepro.2017.04.075
[16]	Mesagan E P, Isola W A, Ajide K B. The capital investment channel of environmental improvement: Evidence from BRICS[J]. Environment Development and Sustainability, 2019, 21:1561-1582. doi: 10.1007/s10668-018-0110-6
[17]	Khan Z, Ali M, Kirikkaleli D, Wahab S, Jiao Z. The impact of technological innovation and public-private partnership investment on sustainable environment in China: Consumption-based carbon emissions analysis[J]. Sustainable Development, 2020, 28(5):1317-1330. doi: 10.1002/sd.v28.5
[18]	Rieber A, Tran T A. Globalization, North-South Industrial Location and Environmental Competition[R]. Working Paper Series No. 2008/02, 2008.
[19]	于淑波, 巩鲁宁, 王可. 基于城镇消费者视角的消费污染及协调性评价[J]. 山东财经大学学报, 2017, 29(1):52-61.
	[ Yu S B, Gong L N, Wang K. Consumption pollution and coordination evaluation from urban consumer perspective[J]. Journal of Shandong University of Finance and Economics, 2017, 29(1):52-61.]
[20]	孙早, 许薛璐. 产业创新与消费升级:基于供给侧结构性改革视角的经验研究[J]. 中国工业经济, 2018, (7):98-116.
	[ Sun Z, Xu X L. Industrial innovation and consumption upgrade: An empirical study based on the perspective of supply-side structural reform[J]. China Industrial Economics, 2018, (7):98-116.]
[21]	Tone K, Chang T S, Wu C H. Handling negative data in slacks: Based measure data envelopment analysis models[J]. European Journal of Operational Research, 2020, 282(3):926-935. doi: 10.1016/j.ejor.2019.09.055
[22]	马骏, 李夏, 张忆君. 江苏省环境效率及其影响因素研究: 基于超效率SBM-ML-Tobit模型[J]. 南京工业大学学报(社会科学版), 2019, 18(2):71-80.
	[ Ma J, Li X, Zhang Y J. Study on environmental efficiency and its influence factors of Jiangsu Province based on SBM-ML-Tobit model[J]. Journal of Nanjing Tech University (Social Science Edition), 2019, 18(2):71-80.]
[23]	闫庆友, 桂增侃, 张文华, 等. 中国能源影子价格和能源环境效率省际差异[J]. 资源科学, 2020, 42(6):1040-1051.
	[ Yan Q Y, Gui Z K, Zhang W H, et al. The heterogeneity of regional energy shadow price and energy environment efficiency in China[J]. Resources Science, 2020, 42(6):1040-1051.]
[24]	何枫, 马栋栋, 徐晓宁. 雾霾约束下中国省际区间环境技术效率研究: 基于SBM-Undesirable区间模型的面板数据分析[J]. 干旱区资源与环境, 2016, 30(12):28-33.
	[ He F, Ma D D, Xu X N. Interval environmental efficiency across provinces in China under the constraint of haze with SBM-undesirable interval model[J]. Journal of Arid Land Resources and Environment, 2016, 30(12):28-33.]
[25]	Zhao H Y, Geng G N, Zhang Q, et al. Inequality of household consumption and air pollution-related deaths in China[J]. Nature Communications, 2019, 10(1):1-9. doi: 10.1038/s41467-018-07882-8
[26]	陈建宝, 李坤明. 收入分配、人口结构与消费结构:理论与实证研究[J]. 上海经济研究, 2013, 25(4):74-87.
	[ Chen J B, Li K M. Income distribution, population structure and consumption structure: Theoretical and empirical study[J]. Shanghai Journal of Economics, 2013, 25(4):74-87.]
[27]	成定平. 投资结构调控与加快服务业发展研究[J]. 经济学家, 2015, (2):44-52.
	[ Cheng D P. A study on the regulation on investment structure and speeding up the development of the service industry[J]. Economist, 2015, (2):44-52.]
[28]	Midelfart K H, Overman H G, Redding S J, et al. The Location of European Industry[R]. European Commission: Economic Papers No. 142, 2000.
[29]	王菲, 董锁成, 毛琦梁, 等. 宁蒙沿黄地带产业结构的环境污染特征演变分析[J]. 资源科学, 2014, 36(3):620-631.
	[ Wang F, Dong S C, Mao Q L, et al. Analysis of the environment-pollution bias of industrial structure in areas along the Yellow River in Ningxia and Inner Mongolia[J]. Resources Science, 2014, 36(3):620-631.]
[30]	Shan Y, Huang Q, Guan D, et al. China CO₂ emission accounts 2016-2017[J/OL]. Scientific Data, (2019-09-09) [2020-05-25]. https://figshare.com/collections/China_CO₂_emission_accounts_2016-2017/4658168.
[31]	Ren S G, Li X L, Yuan B L, et al. The effects of three types of environmental regulation on eco-efficiency: A cross-region analysis in China[J]. Journal of Cleaner Production, 2018, 173:245-255. doi: 10.1016/j.jclepro.2016.08.113
[32]	梁涵玮, 倪玥琦, 董亮, 等. 经济增长与资源消费的脱钩关系: 基于演化视角的中日韩美比较研究[J]. 中国人口·资源与环境, 2018, 28(5):8-16.
	[ Liang H W, Ni Y Q, Dong L, et al. Decoupling relationship analysis between economic growth and resource consumption in China, Japan, South Korea and the United States: A transitional perspective[J]. China Population, Resources and Environment, 2018, 28(5):8-16.]
[33]	曹和平, 奚剑明, 陈玥卓. 城镇居民对环境治理的边际支付意愿[J]. 资源科学, 2020, 42(5):801-811.
	[ Cao H P, Xi J M, Chen Y Z. Urban residents’ marginal willingness to pay for environmental pollution control[J]. Resources Science, 2020, 42(5):801-811.]
[34]	王平, 王琴梅. 消费金融驱动城镇居民消费升级研究: 基于结构与质的多重响应[J]. 南京审计大学学报, 2018, 15(2):69-77.
	[ Wang P, Wang Q M. A research on the upgrading of urban residents consumption driven by consumer finance: Based on the multiple responses of structure and quality[J]. Journal of Nanjing Audit University, 2018, 15(2):69-77.]

	变量名称	变量说明	均值	标准差	极小值	极大值
被解释变量	EE	环境效率	1.043	0.376	0.503	2.363
核心解释变量	sconsu	生存型消费占比	0.709	0.027	0.624	0.799
	xconsu	享受型消费占比	0.168	0.019	0.120	0.208
	fconsu	发展型消费占比	0.123	0.019	0.078	0.177
	ind-agr	二产投资与一产投资之比	20.481	32.392	2.321	366.162
	ser-ind	三产投资与二产投资之比	1.731	1.455	0.602	10.123
	IB	投资结构偏向指数	3.951	1.615	1.345	9.584
控制变量	ER	环境污染治理投资占GDP比重/%	1.338	0.669	0.300	4.240
	agdp	人均GDP/万元	3.531	2.429	0.370	12.899
	techno	R&D投资占GDP比重/%	0.310	0.255	0.034	1.431
	pop	常住人口密度/（千人/km²）	0.441	0.639	0.007	3.851

变量	FE-IV			Sys-GMM
变量	模型1	模型2	模型3	模型4	模型5	模型6
L.ln(EE)				0.945*** (0.036)	0.963*** (0.061)	0.939*** (0.033)
ln(sconsu)	-0.820* (0.412)			-0.350** （0.174）
ln(xconsu)		0.082 (0.218)			-0.039 (0.054)
ln(fconsu)			0.194* (0.634)			0.106** (0.0515)
ln(ER)	-0.074*** (0.020)	-0.072*** (0.020)	-0.073*** (0.019)	-0.028** (0.014)	-0.012 (0.016)	-0.026* (0.016)
ln(agdp)	0.065*** (0.021)	0.062*** (0.023)	0.061*** (0.020)	0.020** (0.010)	0.023** (0.012)	0.029*** (0.010)
ln(techno)	-0.007 (0.014)	-0.012 (0.016)	0.002 (0.014)	0.003 (0.006)	-0.005 (0.004)	0.001 (0.006)
ln(pop)	-0.127 (0.217)	-0.265 (0.215)	-0.193 (0.202)	-0.007 (0.005)	-0.005 (0.005)	-0.011 (0.029)
第一阶段回归F值	55.950***	54.960***	101.560***
Hansen检验	0.119	0.538	0.102	0.996	0.994	1.000
AR(1)				0.058	0.063	0.058
AR(2)				0.200	0.192	0.197
样本量	390	390	390	420	420	420

变量	模型1	模型2	模型3	模型4
L.ln(EE)	0.653***(0.134)	0.988***(0.044)	0.930***(0.061)	0.919***(0.049)
ln(sconsu)		-2.379*(1.270)
ln(xconsu)			0.636***(0.228)
ln(fconsu)				0.074(0.239)
ln(ind-agr)	0.035*(0.020)	0.268*(0.143)	-0.379***(0.143)	-0.016(0.165)
ln(ser-indr)	0.058*(0.032)	0.109(0.088)	-0.132(0.125)	-0.061*(0.034)
ln(sconsu)×ln(ind-agr)		0.751*(0.403)
ln(sconsu)×ln(ser-indr)		0.261(0.234)
ln(xconsu)×ln(ind-agr)			-0.218***(0.080)
ln(xconsu)×ln(ser-indr)			-0.088(0.065)
ln(fconsu)×ln(ind-agr)				-0.011(0.083)
ln(fconsu)×ln(ser-indr)				0.040**(0.020)
Hansen检验	0.386	0.911	0.860	1.000
AR(1)	0.071	0.065	0.070	0.059
AR(2)	0.181	0.217	0.181	0.192
控制变量	是	是	是	是
样本量	420	420	420	420

变量	模型1	模型2	模型3	模型4
L.ln(EE)	0.892***(0.063)	0.981***(0.041)	0.977***(0.035)	0.971***(0.036)
ln(sconsu)		-1.146*(0.611)
ln(xconsu)			0.797**(0.380)
ln(fconsu)				0.292**(0.105)
ln(IB)	-0.007(0.019)	0.274**(0.125)	-1.097**(0.524)	-0.417***(0.162)
ln(sconsu)×ln(IB)		0.791**(0.398)
ln(xconsu)×ln(IB)			-0.602**(0.289)
ln(fconsu)×ln(IB)				-0.187**(0.075)
Hansen检验	0.386	0.938	0.998	1.000
AR(1)	0.063	0.060	0.053	0.060
AR(2)	0.197	0.239	0.272	0.213
控制变量	是	是	是	是
样本量	420	420	420	420

变量	增加控制变量				消费升级指数
变量	模型1	模型2	模型3		模型4	模型5	模型6
L.ln(EE)	0.908***(0.064)	0.921***(0.069)	0.874***(0.054)	0.954***(0.056)		0.906***(0.074)	0.905***(0.070)
ln(sconsu)	-0.437*(0.255)
ln(xconsu)		0.031(0.197)
ln(fconsu)			0.136*(0.071)
ln(index)				0.157**(0.074)		0.139(0.603)	0.775***(0.287)
ln(ind-agr)						-0.022(0.041)
ln(ser-indr)						-0.009(0.018)
ln(IB)							-0.129***(0.047)
ln(index)×ln(ind-agr)						0.046(0.174)
ln(index)×ln(ser-indr)						0.208**(0.102)
ln(index)×ln(IB)							-0.790***(0.241)
ln(ER)	-0.020(0.017)	-0.014(0.017)	-0.020(0.013)	-0.011(0.013)		-0.007(0.015)	-0.017(0.010)
ln(agdp)	0.073**(0.033)	0.062*(0.035)	0.061***(0.026)	0.034***(0.013)		0.029**(0.014)	0.013(0.018)
ln(techno)	-0.002(0.006)	-0.005(0.005)	0.001(0.007)	-0.002(0.004)		-0.009*(0.005)	-0.003(0.008)
ln(pop)	-0.004(0.007)	-0.001(0.006)	-0.009(0.010)	-0.008(0.007)		-0.013(0.010)	0.013(0.018)
ln(edu)	-0.178*(0.097)	-0.141*(0.75)	-0.143(0.107)
ln(fdi)	0.007(0.005)	0.008(0.006)	0.009(0.101)
ln(retail)	-0.018*(0.010)	-0.016(0.011)	-0.021**(0.009)
Hansen检验	0.942	0.871	0.998	0.998		0.923	0.954
AR(1)	0.059	0.062	0.053	0.060		0.062	0.046
AR(2)	0.217	0.206	0.272	0.206		0.177	0.117
样本量	420	420	420	420		420	420