中国省际碳影子价格与碳生产率非线性关联研究

doi:10.18402/resci.2018.10.19

摘要/Abstract

摘要：

当各地区减排成本增速高于经济增速时,单一碳排放效率指标已无法完整刻画减排情况。本文基于全局非径向方向性距离函数（Global NDDF）及其对偶原理测算得出2010—2015年中国全要素碳排放效率总体呈上升趋势,其中东部地区效率最高,而东北与西部地区效率最低;全国各地区总的碳减排成本增长率与经济增速比值呈波动上升趋势,且大部分地区总减排成本增速大于经济增速。这表明碳排放效率提升是以高于经济增速的减排成本投入实现的。数理推导结论表明,解决减排成本增速远超GDP增速的困境,需满足碳影子价格增速小于碳生产率增速。计量模型进一步证明,中国碳影子价格增长速度高于碳生产率增速导致了减排成本增速快于经济增速的困境。为解决这一问题,应以多元指标体系替代单一的碳排放效率指标,全面衡量减排能力;构建全国碳交易市场以缓解各地区碳影子价格异质性现象;持续推动结构性改革和产业结构转型升级,提高能源利用效率。

关键词: 碳排放效率, 碳影子价格, 碳生产率, 全局非径向方向性距离函数, 中国

Abstract:

When the abatement cost growth rate is higher than the economic growth rate, the single carbon emission efficiency index cannot depict the situation of emission abatement. Based on the global non-radial directional distance function (Global NDDF) and its duality principle, the global carbon emissions productivity in China is calculated. For the sample period 2010-2015, the national GCP is in upward trend, with the eastern region having the highest GCP, the northeast and western regions having the lowest; The ratios of abatement cost growth to GDP growth of the provincial administration regions in China rise in volatility, and the abatement cost grows faster than GDP in most regions. This means the carbon efficiency improvement is achieved by the rapid growth of abatement cost, which is higher than the GDP growth. The mathematical deduction shows that the condition to solve the dilemma of abatement cost growth exceeding GDP growth means the growth rate of carbon shadow price is lower than the growth rate of carbon productivity. Empirical results prove further that the overgrowth of carbon productivity by carbon shadow price leads to the dilemma of abatement cost growth exceeding GDP growth in China. Therefore, China should replace the single carbon emission efficiency index by a multiple index system to measure the capacity of abatement in an all-round way; The national carbon trading market should be built to alleviate the heterogeneity of carbon shadow price in different regions; And government should promote structural reform and improve energy efficiency.

Key words: carbon emission efficiency, carbon shadow price, carbon productivity, Global Non-Radial Directional Distance Function, China

王倩, 高翠云. 中国省际碳影子价格与碳生产率非线性关联研究[J]. 资源科学, 2018, 40(10): 2118-2131.

Qian WANG, Cuiyun GAO. Research on the nonlinear correlation between provincial carbon shadow price and carbon productivity[J]. Resources Science, 2018, 40(10): 2118-2131.

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变量/单位	均值	标准差	中位数	最大值	最小值
资本/亿元	10 906.66	9 503.04	7 911.50	52 981.52	638.33
劳动/万人	2 679.91	1 751.10	2 165.80	6 636.08	307.65
能源/万t	14 294.48	8 509.10	11 361.06	39 969.77	1 390.24
GDP/亿元	4 362.28	3 641.85	3 248.42	17 818.91	232.37
CO₂/万t	33 121.28	21 659.93	25 409.91	90 366.86	2 724.91

变量	计算方法
碳生产率（CP）	实际GDP/CO₂排放量
碳排放强度（CI）	CO₂排放量/实际GDP
产业结构（IS）	第二产业增加值/GDP
城镇化水平（URB）	年末城镇人口/总人口
环境污染治理投资（EPI）	环境污染治理投资/GDP
能源结构（ELE）	电力消耗量/总消耗量

地区	2011年	2012年	2013年	2014年	2015年
北京	0.714	0.754	0.795	0.858	1.000
天津	0.428	0.450	0.462	0.507	0.580
河北	0.174	0.188	0.178	0.198	0.212
山西	0.116	0.121	0.120	0.129	0.149
内蒙古	0.107	0.112	0.129	0.137	0.154
辽宁	0.311	0.285	0.280	0.292	0.313
吉林	0.221	0.251	0.274	0.296	0.348
黑龙江	0.463	0.434	0.426	0.369	0.335
上海	0.742	0.822	0.742	1.000	1.000
江苏	0.485	0.516	0.504	0.548	0.603
浙江	0.449	0.465	0.504	0.545	0.595
安徽	0.646	0.587	0.534	0.549	0.590
福建	0.646	0.703	0.703	0.679	0.738
江西	0.367	0.412	0.353	0.379	0.403
山东	0.281	0.301	0.347	0.365	0.398
河南	0.247	0.284	0.322	0.341	0.405
湖北	0.272	0.313	0.390	0.422	0.504
湖南	0.549	0.575	0.611	0.640	0.637
广东	0.569	0.626	0.671	0.722	0.795
广西	0.250	0.264	0.277	0.305	0.360
海南	1.000	0.840	0.685	0.638	0.707
重庆	0.488	0.538	0.636	0.638	0.685
四川	0.826	0.822	0.821	0.885	1.000
贵州	0.127	0.134	0.146	0.169	0.212
云南	0.197	0.206	0.229	0.278	0.369
陕西	0.289	0.296	0.297	0.313	0.377
甘肃	0.234	0.246	0.251	0.270	0.316
青海	0.173	0.173	0.166	0.177	0.196
宁夏	0.079	0.062	0.059	0.061	0.073
新疆	0.116	0.106	0.099	0.098	0.101
全国均值	0.386	0.396	0.400	0.427	0.472
东部均值	0.549	0.567	0.559	0.606	0.663
中部均值	0.366	0.382	0.388	0.410	0.448
西部均值	0.262	0.269	0.283	0.303	0.349
东北均值	0.332	0.323	0.327	0.319	0.332

地区	2011年	2012年	2013年	2014年	2015年
北京	3.462	1.773	1.754	2.167	3.570
天津	0.324	1.431	1.242	2.075	2.678
河北	1.242	1.924	0.297	2.795	2.131
山西	1.476	1.463	0.885	2.608	6.063
内蒙古	-0.016	1.472	2.815	1.844	2.808
辽宁	1.044	0.055	0.779	1.797	3.363
吉林	0.861	2.246	2.184	2.333	3.976
黑龙江	0.616	0.323	0.735	-1.522	-0.693
上海	1.801	2.544	-0.370	6.326	1.000
江苏	1.303	1.699	0.738	2.089	2.287
浙江	0.632	1.483	2.090	2.168	2.232
安徽	1.383	0.150	0.040	1.352	1.932
福建	0.085	1.852	1.000	0.632	2.039
江西	1.263	2.243	-0.563	1.848	1.738
山东	1.633	1.779	2.763	1.655	2.200
河南	1.594	2.655	2.638	1.723	3.450
湖北	0.212	2.481	3.673	1.939	3.380
湖南	0.690	1.451	1.692	1.536	0.946
广东	-0.172	2.319	1.923	2.035	2.373
广西	1.042	1.548	1.535	2.281	3.392
海南	1.000	-0.918	-1.050	0.126	2.487
重庆	0.558	1.853	2.657	1.031	1.745
四川	1.951	0.956	0.995	1.998	2.769
贵州	-2.471	1.415	1.847	2.618	3.569
云南	1.679	1.382	2.019	3.881	5.075
陕西	2.674	1.210	1.037	1.599	3.806
甘肃	1.351	1.444	1.244	1.898	3.272
青海	0.349	0.991	0.582	1.783	2.413
宁夏	-1.387	-1.082	0.485	1.495	3.557
新疆	0.255	0.222	0.367	0.845	1.396
全国均值	0.881	1.346	1.268	1.899	2.698
东部均值	1.131	1.589	1.039	2.207	2.300
中部均值	1.103	1.741	1.394	1.834	2.918
西部均值	0.544	1.037	1.417	1.934	3.073
东北均值	0.840	0.875	1.232	0.869	2.215

变量	模型1	模型2	模型3	模型4	模型5
CP	0.021	0.047	0.121**	0.150***	0.138**
	（0.59）	（1.20）	（2.28）	（2.79）	（2.57）
（CP）²	1.243***	1.220***	1.084***	1.039***	1.077***
	（15.89）	（15.42）	（10.89）	（10.39）	（10.72）
IS		0.021
		（1.64）
URB			-0.053**	-0.064***	-0.038
			（-2.52）	（-3.00）	（-1.56）
EPI				0.002**	0.002***
				（2.31）	（2.61）
ELE					-0.092**
					（-2.09）
Cons	0.003	-0.010	0.022***	0.023***	0.020**
	（0.90）	（-1.18）	（2.68）	（2.79）	（2.45）
R²	0.928	0.929	0.931	0.933	0.935
F值	948.660	640.520	657.380	508.940	417.430
Prob>F	0.000	0.000	0.000	0.000	0.000