中国采矿业绿色发展的动力特征与政策启示——基于企业异质性视角

doi:10.18402/resci.2022.03.10

摘要/Abstract

摘要：

探析异质性矿企绿色发展的动力特征能够为评估和制定采矿业绿色发展政策提供参考。本文以2003—2018年沪深A股采矿业上市公司为研究对象,利用随机前沿模型测算了企业考虑资源和环境双重负外部性的绿色全要素生产率增长及其分解结果,据此讨论了不同规模和所有制矿企绿色发展动力特征的演化及其政策含义。研究发现：①总体上,平均规模效应是驱动中国采矿业上市公司绿色全要素生产率增长的主要动力,但规模效应下降及大幅波动也在制约其可持续增长,技术进步虽然稳步提升,但目前作用甚微;②国有矿企存在技术进步相对优势,但规模效应并不突出,而近年来非国有矿企的规模效应快速改善,并推动其在能源开采业和金属矿采选业中绿色全要素生产率增长超过国企;③相对于中小矿企,大型矿企具有规模和技术优势,但受规模效应下降影响,其绿色发展前景不容乐观,这在能源开采业和金属矿采选业中均得到验证。据此,本文梳理了2000年以来政府出台的与矿企规模和所有制相关的政策,发现这些政策因忽视异质性矿企的绿色发展差异而作用有限,未来政策制定应当考虑消除规模和所有制歧视,加快实施创新驱动战略。

关键词: 采矿业, 上市公司, 绿色全要素生产率, 规模效应, 技术进步, 企业规模, 所有制

Abstract:

Clarifying the dynamic characteristics of heterogeneous mining enterprises' green development can provide insights for evaluating and formulating green mining policies. Taking A-share listed mining enterprises in Shanghai and Shenzhen from 2003 to 2018 as an example, this study used the stochastic frontier model to calculate the growth and decomposition results of Green Total Factor Productivity (GTFP) of heterogeneous mining enterprises considering the negative externalities of resources and environment, and discussed the dynamic characteristics of green development of mining enterprises and their policy implications. The results show that: (1) Overall, the average scale effect is the main force driving the growth of the GTFP of China's listed mining companies, but the decline and sharp fluctuation of the scale effect restrict the green and sustainable development of mining industries. Although technological progress has steadily improved, its effect is very weak. (2) State-owned mining enterprises have relative advantages in technological progress, but the scale effect is not prominent. The scale effect of non-state-owned mining enterprises has improved rapidly in recent years, driving their GTFP growth in energy and metal mineral industries to exceed that of state-owned enterprises. (3) Compared with small and medium-sized mining enterprises, large enterprises have advantages in scale economies and technological progress, but their prospects are not optimistic due to the sharp decline in scale effect, which has been verified in energy and metal mineral industries. Furthermore, this study examined the mining green development policies related to enterprise scale and ownership issued by the government since 2000, and we found that these policies may have limited effects by ignoring the differences of green development dynamics among enterprises. Future policies should consider eliminating discrimination of scale and ownership, and accelerate the implementation of innovation driven strategy.

Key words: mining industry, listed companies, green total factor productivity, scale effect, technological progress, enterprise scale, ownership

于立宏, 金玉健. 中国采矿业绿色发展的动力特征与政策启示——基于企业异质性视角[J]. 资源科学, 2022, 44(3): 554-569.

YU Lihong, JIN Yujian. Dynamic characteristics of green development in China's mining industry and policy implications: From the perspective of firm heterogeneity[J]. Resources Science, 2022, 44(3): 554-569.

图/表 12

表1

表2

模型检验结果

检验项目	假设	$L (H 0)$	LR统计量	1%临界值	检验结论
①	$H 0 : γ = μ = 0$	-745.00	932.02	10.50	拒绝
②	$H 0 : η = 0$	-284.60	11.22	8.27	拒绝
③	$H 0 : β w v = 0 (w 、 v = K, Y, R, E, T)$	-376.53	195.09	29.93	拒绝
④	$H 0 : β T = β T w = 0 (w = K, Y, R, E, T)$	-297.62	37.26	16.07	拒绝
⑤	$H 0 : β T h = 0 (h = K, Y, R, E)$	-286.05	14.12	12.48	拒绝

表2

表3

模型估计结果

参数	系数	标准误	z值	P值
$β Y$	0.6743	0.3490	1.9300	0.0530
$β X K$	0.9050	0.2815	3.2200	0.0010
$β R$	0.1357	0.0700	1.9400	0.0520
$β E$	0.0974	0.1648	0.5900	0.5550
$β Y Y$	0.0166	0.0151	1.1000	0.2700
$β X K X K$	0.0306	0.0048	6.3500	0.0000
$β R R$	-0.0018	0.0006	-2.7700	0.0060
$β E E$	-0.0174	0.0037	-4.6500	0.0000
$β Y X K$	-0.0736	0.0173	-4.2600	0.0000
$β Y R$	-0.0015	0.0060	-0.2400	0.8070
$β Y E$	0.0414	0.0114	3.6300	0.0000
$β X K R$	-0.0065	0.0055	-1.1800	0.2400
$β X K E$	-0.0213	0.0109	-1.9600	0.0500
$β R E$	0.0028	0.0029	0.9600	0.3360
$β T$	0.0388	0.0452	0.8600	0.3900
$β T T$	-0.0027	0.0008	-3.5000	0.0000
$β T Y$	0.0023	0.0027	0.8500	0.3970
$β T R$	-0.0020	0.0009	-2.3900	0.0170
$β T E$	-0.0036	0.0020	-1.7900	0.0730
$β 0$	-18.2863	3.4658	-5.2800	0.0000

表3

图1

图2

图3

图4

图5

图6

图7

图8

图9

参考文献 40

[1]	柳晓娟, 侯华丽, 孙映祥, 等. 关于中国绿色矿业内涵与实现路径的思考[J]. 矿业研究与开发, 2021, 41(10): 180-186.
	[ Liu X J, Hou H L, Sun Y X, et al. Thinking about the connotation and realization path for green mining in China[J]. Mining Research and Development, 2021, 41(10): 180-186.]
[2]	王兵, 杨欣怡. 中国工业行业全要素生产率分析(1981-2015): 波特假说的验证[J]. 产经评论, 2019, 10(6): 87-107.
	[ Wang B, Yang X Y. Analysis on green total factor productivity of China's industry (1981-2015): Prove the Porter-hypothesis[J]. Industrial Economic Review, 2019, 10(6): 87-107.]
[3]	Zhang L, Gao W T, Chiu Y H, et al. Environmental performance indicators of China's coal mining industry: A bootstrapping Malmquist index analysis[J]. Resources Policy, 2021, DOI: 10.1016/j.resourpol.2021.101991. doi: 10.1016/j.resourpol.2021.101991
[4]	Fang C D, Cheng J H, Zhu Y G, et al. Green total factor productivity of extractive industries in China: An explanation from technology heterogeneity[J]. Resources Policy, 2021, DOI: 10.1016/j.resourpol.2020.101933. doi: 10.1016/j.resourpol.2020.101933
[5]	谢雄标, 吴越, 冯忠垒, 等. 中国资源型企业绿色行为调查研究[J]. 中国人口·资源与环境, 2015, 25(6): 5-11.
	[ Xie X B, Wu Y, Feng Z L, et al. Investigation of green behavior of resource-based enterprise in China[J]. China Population, Resources and Environment, 2015, 25(6): 5-11.]
[6]	Zhou M, Govindan K, Xie X B, et al. How to drive green innovation in China's mining enterprises? Under the perspective of environmental legitimacy and green absorptive capacity[J]. Resources Policy, 2021, DOI: 10.1016/j.resourpol.2021.102038. doi: 10.1016/j.resourpol.2021.102038
[7]	王永卿, 王来峰, 邓洪星, 等. 湖北省绿色矿山建设影响因素及其效果分析[J]. 资源科学, 2019, 41(8): 1513-1525.
	[ Wang Y Q, Wang L F, Deng H X, et al. Influencing factors and performance evaluation of green mining in Hubei Province[J]. Resources Science, 2019, 41(8): 1513-1525.]
[8]	姚小剑, 夏丹丹, 张英琳. 环境规制对陕西省能源开发产业绿色全要素生产率影响研究[J]. 中国矿业, 2021, 30(5): 100-106.
	[ Yao X J, Xia D D, Zhang Y L. Research on the influence of environmental regulation on total factor productivity of Shaanxi energy development industry[J]. China Mining Magazine, 2021, 30(5): 100-106.]
[9]	李静, 王姝兰. 基于偏向型技术进步的中国工业绿色生产率及分解: 兼论偏向型技术的节水和减排效应[J]. 华东经济管理, 2020, 34(9): 30-42.
	[ Li J, Wang S L. Measurement and decomposition of China's industry green productivity based on biased technological change: With a discussion on energy-saving and emission-reduction effect of biased technological change[J]. East China Economic Management, 2020, 34(9): 30-42.]
[10]	Cui H R, Wang H R, Zhao Q Z. Which factors stimulate industrial green total factor productivity growth rate in China? An industrial aspect[J]. Greenhouse Gases: Science and Technology, 2019, 9(3): 505-518. doi: 10.1002/ghg.1874
[11]	任思达, 刘倩, 李玫洁. 中国矿业经济绿色发展评价研究[J]. 中国国土资源经济, 2019, 32(12): 66-72.
	[ Ren S D, Liu Q, Li M J. Research on evaluation of green development of China's mining economy[J]. Natural Resource Economics of China, 2019, 32(12): 66-72.]
[12]	王克强, 武英涛, 刘红梅. 中国能源开采业全要素生产率的测度框架与实证研究[J]. 经济研究, 2013, 48(6): 127-140.
	[ Wang K Q, Wu Y T, Liu H M. An analytical framework and empirical study for TFP in Chinese energy mining industry[J]. Economic Research Journal, 2013, 48(6): 127-140.]
[13]	于立宏, 王艳, 陈家宜. 考虑环境和代际负外部性的中国采矿业绿色全要素生产率[J]. 资源科学, 2019, 41(12): 2155-2171.
	Yu L H, Wang Y, Chen J Y. Green total factor productivity of Chinese mining industries considering negative intergenerational and environmental externalities[J]. Resources Science, 2019, 41(12): 2155-2171.]
[14]	王晓祺, 宁金辉. 强制社会责任披露能否驱动企业绿色转型? 基于我国上市公司绿色专利数据的证据[J]. 审计与经济研究, 2020, 35(4): 69-77.
	[ Wang X Q, Ning J H. Can mandatory social responsibility disclosure drive corporate green transformation? Evidence based on green patent data of listed companies in China[J]. Journal of Audit & Economics, 2020, 35(4): 69-77.]
[15]	高翠云, 王倩. 绿色经济发展与政府环保行为的互动效应[J]. 资源科学, 2020, 42(4): 776-789.
	[ Gao C Y, Wang Q. Effects of interactions of green economic development and government environmental protection behavior[J]. Resources Science, 2020, 42(4): 776-789.]
[16]	卞元超, 吴利华, 白俊红, 等. 要素市场扭曲是否抑制了绿色经济增长?[J]. 世界经济文汇, 2021, (2): 105-119.
	[ Bian Y C, Wu L H, Bai J H, et al. Does factor market distortion restrain the green economic growth?[J]. World Economic Papers, 2021, (2): 105-119.]
[17]	Rodriguez X A, Arias C. The effects of resource depletion on coal mining productivity[J]. Energy Economics, 2008, 30(2): 397-408. doi: 10.1016/j.eneco.2007.10.007
[18]	Song M L, Peng L C, Shang Y P, et al. Green technology progress and total factor productivity of resource-based enterprises: A perspective of technical compensation of environmental regulation[J]. Technological Forecasting and Social Change, 2022, DOI: 10.1016/j.techfore.2021.121276. doi: 10.1016/j.techfore.2021.121276
[19]	Rodríguez X A, Maria L L, Arias C. Measuring productivity in the extractive industries: Evidence from Spanish fluorite mining[J]. Resources Policy, 2021, DOI: 10.1016/j.resourpol.2021.102187. doi: 10.1016/j.resourpol.2021.102187
[20]	Li J C, Yu L H. How does state-owned shares affect double externalities and industrial performance: Evidence from China's exhaustible resources industry[J]. Journal of Cleaner Production, 2018, 176: 920-928. doi: 10.1016/j.jclepro.2017.12.029
[21]	陈创练, 马子柱, 单敬群. 中国技术进步偏向、要素配置效率与产业结构转型升级[J]. 产经评论, 2021, 12(6): 47-58.
	[ Chen C L, Ma Z Z, Shan J Q. Technological progress direction, the efficiency of resource allocation and the transformation of industrial structure in China[J]. Industrial Economic Review, 2021, 12(6): 47-58.]
[22]	Battese G E, Coelli T J. Frontier production functions, technical efficiency and panel data: With application to paddy farmers in India[J]. Journal of Productivity Analysis, 1992, 3(1): 153-169. doi: 10.1007/BF00158774
[23]	Ju K Y, Wang Q W, Liu L F, et al. Measurement of the price distortion degree for exhaustible energy resources in China: A discount rate perspective[J]. Emerging Markets Finance and Trade, 2019, 55(12): 2718-2737. doi: 10.1080/1540496X.2019.1587708
[24]	Zhao H R, Li B K, Wang X J, et al. Evaluating the performance of China's coal-fired power plants considering the coal depletion cost: A system dynamic analysis[J]Journal of Cleaner Production, 2020, DOI: 10.1016/j.jclepro.2020.122809. doi: 10.1016/j.jclepro.2020.122809
[25]	Serafy E S. Absorptive capacity, the demand for revenue, and the supply of petroleum[J]. The Journal of Energy and Development, 1981, 7(1): 73-88.
[26]	Seraty E S. The Proper Calculation of Income from Depletable Natural Resources[C]. Washington: World Bank, 1989.
[27]	于立宏, 李嘉晨. 双重外部性约束下中国资源型企业绩效研究[J]. 中国人口·资源与环境, 2016, 26(4): 63-72.
	[ Yu L H, Li J C. Resources listed enterprises' performance under the constrains of duel externalities[J]. China Population, Resources and Environment, 2016, 26(4): 63-72.]
[28]	郭江, 铁卫, 李国平. 运用CVM评估煤炭矿区生态环境外部成本的测算尺度选择研究: 基于有效性和可靠性分析视角[J]. 生态经济, 2018, 34(8): 163-168.
	Guo J, Tie W, Li G P. A study on the selection of measurement scale using CVM to evaluate the external cost of ecological environment in coal mining area: Based on validity and reliability analysis[J]. Ecological Economy, 2018, 34(8): 163-168.]
[29]	吴文洁, 常志风. 油气资源开发生态补偿标准模型研究[J]. 中国人口·资源与环境, 2011, 21(5): 26-30.
	[ Wu W J, Chang Z F. Research on eco-compensation standard model for oil-gas exploitation[J]. China Population, Resources and Environment, 2011, 21(5): 26-30.]
[30]	贾舒娴, 黄健柏, 钟美瑞. 生态文明建设背景下江西省有色金属矿产开发生态影响能值分析[J]. 长江流域资源与环境, 2017, 26(9): 1378-1387.
	Jia S X, Huang J B, Zhong M R. Energy analysis of the development of nonferrous metals in Jiangxi Province under the background of ecological civilization construction[J]. Resources and Environment in the Yangtze Basin, 2017, 26(9): 1378-1387.]
[31]	周吉光, 丁欣. 河北省矿产资源开采造成的环境损耗的经济计量[J]. 资源与产业, 2012, 14(6): 148-155.
	[ Zhou J G, Ding X. Economic measurement of environmental loss brought by mineral resources exploitation in Hebei Province[J]. Resource & Industries, 2012, 14(6): 148-155.]
[32]	李国平, 李恒炜. 使用者成本法的完善与美国油气资源使用者成本的估算[J]. 自然资源学报, 2013, 28(6): 1046-1058.
	[ Li G P, Li H W. The perfection of user cost approach and the estimation of user cost of oil and gas resources of America[J]. Journal of Natural Resources, 2013, 28(6): 1046-1058.]
[33]	曾先峰, 李国平. 非再生能源资源使用者成本: 一个新的估计[J]. 资源科学, 2013, 35(2): 439-446.
	[ Zeng X F, Li G P. A new estimation of user costs for non-renewable energy resources[J]. Resources Science, 2013, 35(2): 439-446.]
[34]	李国平, 吴迪. 使用者成本法及其在煤炭资源价值折耗测算中的应用[J]. 资源科学, 2004, (3): 123-129.
	[ Li G P, Wu D. User cost approach and its application for measuring depletion of coal in China[J]. Resources Science, 2004, (3): 123-129.]
[35]	芈凌云, 杨洁. 中国居民生活节能引导政策的效力与效果评估: 基于中国1996–2015年政策文本的量化分析[J]. 资源科学, 2017, 39(4): 651-663.
	Mi L Y, Yang J.. Evaluation of policy efficacy and effect of resident energy conservation based on policy quantification in China from 1996 to 2015[J]. Resources Science, 2017, 39(4): 651-663.]
[36]	Akhmadi S, Tsakalerou M. Innovation propensity and firm size: Evidence from manufacturing[J]. Procedia Manufacturing, 2021, 55: 543-549. doi: 10.1016/j.promfg.2021.10.074
[37]	成琼文, 丁红乙. 政府补贴强度对资源型企业实质性创新产出的影响[J]. 科技进步与对策, 2021, 38(2): 85-94.
	[ Cheng Q W, Ding H Y. Research on the influence of government subsidy intensity on the substantial innovation output of resource-based enterprises[J]. Science & Technology Progress and Policy, 2021, 38(2): 85-94.]
[38]	解学梅, 王宏伟. 网络嵌入对企业创新绩效的影响机理: 一个基于非研发创新的有调节中介模型[J]. 管理工程学报, 2020, 34(6): 13-28.
	[ Xie X M, Wang H W. The impact mechanism of network embeddedness on firm innovation performance: A moderated mediation model based on non-R&D innovation[J]. Journal of Industrial Engineering and Engineering Management, 2020, 34(6): 13-28.]
[39]	于立宏, 牟伯中, 李嘉晨. 中国页岩气发展战略与政策体系研究[M]. 上海: 华东理工大学出版社, 2017.
	[ Yu L H, Mou B Z, Li J C. Research on China's Shale Gas Development Strategy and Policy System[M]. Shanghai: East China University of Science and Technology Press, 2017.]
[40]	侯华丽, 柳晓娟, 郭冬艳, 等. 全国绿色矿山名录分析与政策建议[J]. 中国矿业, 2020, 29(6): 1-7.
	[ Hou H L, Liu X J, Guo D Y, et al. Analysis and suggestions on the national directory of green mines[J]. China Mining Magazine, 2020, 29(6): 1-7.]

变量符号	含义	全样本		大型企业组别		中小企业组别		国企组别		非国企组别
变量符号	含义	均值	标准差	均值	标准差	均值	标准差	均值	标准差	均值	标准差
VC	可变成本	9.16	1.55	10.09	1.09	8.25	1.39	9.54	1.29	7.57	1.56
Y	产出水平	22.57	1.81	23.78	1.48	21.38	1.22	22.89	1.73	21.25	1.51
P_K	资本价格	-6.45	1.03	-6.54	0.93	-6.36	1.12	-6.39	0.98	-6.71	1.20
P_L	劳动力价格	9.03	0.87	9.12	0.78	8.94	0.94	8.98	0.81	9.23	1.06
X_K	资本投入量	21.76	1.78	22.95	1.43	20.58	1.22	22.01	1.75	20.67	1.49
R	资源外部性成本	8.88	4.74	10.10	3.75	7.68	5.28	8.79	5.17	9.25	2.08
E	环境外部性成本	10.45	2.19	11.58	1.46	9.34	2.23	10.97	1.83	8.28	2.27