基于碳交易与绿色信贷的化工企业低碳行为决策研究

doi:10.16381/j.cnki.issn1003-207x.2022.2715

Abstract

Abstract:

As countries around the world attach importance to low-carbon economy， in order to effectively reduce carbon emissions， governments have implemented a variety of carbon policies including carbon tax， carbon trading market mechanism and low-carbon subsidies， and established a carbon emissions trading market. Carbon dioxide and other greenhouse gases are the main causes of global warming， and the chemical industry has a significant high energy consumption， high pollution， high carbon emissions of the “three high”characteristics， so in China to carry out carbon peak， carbon neutralization action and the construction of carbon emissions trading market in the chemical industry should also be the focus of the field. As the main financial service subject supporting the “double carbon” goal of the chemical industry， commercial banks are facing the “three high” characteristics of the chemical industry. How to set the green credit delivery standard has become the key to the financial service and support of the “double carbon” goal.From the perspective of green credit master-slave game crossover， considering the carbon trading standards formulated by the government， a Stackelberg game model of credit behavior between commercial banks and chemical enterprises under the carbon quota trading policy is constructed， in which commercial banks are the leaders and chemical enterprises are followers. The optimal decisions of each decision-maker in three situations （ chemical enterprises actively reduce emissions and sell surplus carbon emission quotas， chemical enterprises negatively reduce emissions and purchase carbon emission quotas， chemical enterprises negatively reduce emissions but do not purchase carbon emission quotas ） are mainly discussed. By comparing the profit functions under different modes， the influence of carbon trading price and carbon quota standard on the behavior decision-making of chemical enterprises and commercial banks is analyzed. Finally， the influence mechanism of carbon trading price， carbon quota standard and green credit policy on the low-carbon optimal decision-making of chemical enterprises is deeply explored by numerical simulation， and the theoretical analysis part of this paper is further verified.The results show that　（1）when the carbon trading price is higher than a certain critical value， chemical enterprises can obtain higher profits when they implement strict carbon emission strategies and actively trade in the carbon trading market， and the profits of commercial banks also increase at this time， which is in line with the government 's green planning standards. （2）Too low or too high carbon emission quotas are not conducive to chemical enterprises to actively carry out green technology innovation. When the carbon emission quota is too high， chemical enterprises are more willing to purchase carbon quotas in the carbon trading market.（3） Under the joint action of carbon trading price and carbon emission quota， when the carbon price is high， the carbon emission quota will bring more profits to the chemical enterprises with strict emission reduction when it is higher than a certain critical value. Commercial banks are also more willing to give green credit， and the total social welfare is optimal.

Key words: green credit, Stackelberg, carbon trading, chemical enterprises, low carbon standard

CLC Number:

F426.7

Tingqiang Chen, Wenjuan Lin, Lei Wang, Lean Yu. Low-carbon Behavior Decision of Chemical Enterprises Based on Carbon Trading and Green Credit[J]. Chinese Journal of Management Science, 2025, 33(6): 322-334.

Figures/Tables 15

	模式S	模式L	模式N
化工企业最优决策 $p ̂$	$p ̂ 1 = a + ∫ - ∞ 0 F x d x + c 0 + k M - w 1 ⋅ b ⋅ 1 + r 1 2 b$	$p ̂ 2 = a + ∫ - ∞ 0 F x d x + c 0 ⋅ b ⋅ 1 + r 1 2 b$	$p ̂ 3 = a + ∫ - ∞ 0 F x d x + c 0 ⋅ b ⋅ 1 + r 2 2 b$
商业银行最优决策 $r ̂$	$r ̂ 1 = a - ∫ - ∞ 0 F x d x 2 b ⋅ c 0 + k M - w 1 + P b 1 ⋅ ρ 2 1 - P b 1 - 12$	$r ̂ 1 = a - ∫ - ∞ 0 F x d x 2 c 0 ⋅ b + P b 2 ⋅ ρ 2 1 - P b 2 - 12$	$r ̂ 2 = 1 - 2 P b 3 ⋅ a - ∫ - ∞ 0 F x d x - c 0 ⋅ b 2 - P b 3 ⋅ c 0 ⋅ b$

参数	描述	基准值	取值范围
$c 0$	初始成本	1	（0，3）
$w 1$	模式S下化工企业的碳排放量	1	（0，3）
$w 2$	模式L及模式N下化工企业的碳排放量	4	（3，6）
$r 1$	模式S及模式L下商业银行的贷款利率	0.03	（0，0.1）
$r 2$	模式N下商业银行的贷款利率	0.04	（0，0.1）
$b$	消费者价格敏感系数	1	（0，5）
$a$	潜在市场需求	6	（0，10）
$ρ$	化工企业违约时商业银行的损失率	0.2	（0，1）
$∫ - ∞ 0 F x d x$	不确定需求的期望	-0.5	（-1，0）

$ε$	$M$										期望	方差
$ε$	0.3	0.6	0.9	1.2	1.5	1.8	2.1	2.4	2.7	3.0	期望	方差
0.2	6.001	5.329	4.687	4.076	3.496	2.946	2.427	1.938	1.480	1.052	3.343	2.786
0.4	5.861	5.249	4.667	4.116	3.596	3.106	2.647	2.218	1.820	1.452	3.473	2.214
0.6	5.721	5.169	4.647	4.156	3.696	3.266	2.867	2.498	2.160	1.852	3.603	1.708
0.8	5.581	5.089	4.627	4.196	3.796	3.426	3.087	2.778	2.500	2.252	3.733	1.268
1.0	5.441	5.009	4.607	4.236	3.896	3.586	3.307	3.058	2.840	2.652	3.863	0.894
1.2	5.301	4.929	4.587	4.276	3.996	3.746	3.527	3.338	3.180	3.052	3.993	0.586
1.4	5.161	4.849	4.567	4.316	4.096	3.906	3.747	3.618	3.520	3.452	4.123	0.344
1.6	5.021	4.769	4.547	4.356	4.196	4.066	3.967	3.898	3.860	3.852	4.253	0.168
1.8	4.881	4.689	4.527	4.396	4.296	4.226	4.187	4.178	4.200	4.252	4.383	0.058
2.0	4.741	4.609	4.507	4.436	4.396	4.386	4.407	4.458	4.540	4.652	4.513	0.015
2.2	4.601	4.529	4.487	4.476	4.496	4.546	4.627	4.738	4.880	5.052	4.643	0.037
2.4	4.461	4.449	4.467	4.516	4.596	4.706	4.847	5.018	5.220	5.452	4.773	0.125
2.6	4.321	4.369	4.447	4.556	4.696	4.866	5.067	5.298	5.560	5.852	4.903	0.279
2.8	4.181	4.289	4.427	4.596	4.796	5.026	5.287	5.578	5.900	6.252	5.033	0.499
3.0	4.041	4.209	4.407	4.636	4.896	5.186	5.507	5.858	6.240	6.652	5.163	0.785
3.2	3.901	4.129	4.387	4.676	4.996	5.346	5.727	6.138	6.580	7.052	5.293	1.137
3.4	3.761	4.049	4.367	4.716	5.096	5.506	5.947	6.418	6.920	7.452	5.423	1.555
3.6	3.621	3.969	4.347	4.756	5.196	5.666	6.167	6.698	7.260	7.852	5.553	2.040
3.8	3.481	3.889	4.327	4.796	5.296	5.826	6.387	6.978	7.600	8.252	5.683	2.590
4.0	3.341	3.809	4.307	4.836	5.396	5.986	6.607	7.258	7.940	8.652	5.813	3.206
期望	4.671	4.569	4.497	4.456	4.446	4.466	4.517	4.598	4.710	4.852
方差	0.686	0.224	0.014	0.056	0.350	0.896	1.694	2.744	4.046	5.600

$ε$	$M$										期望	方差
$ε$	0.3	0.6	0.9	1.2	1.5	1.8	2.1	2.4	2.7	3.0	期望	方差
0.2	9.352	9.414	9.476	9.538	9.599	9.661	9.723	9.785	9.847	9.908	9.630	0.035
0.4	8.590	8.714	8.837	8.961	9.084	9.208	9.332	9.455	9.579	9.702	9.146	0.140
0.6	7.828	8.013	8.199	8.384	8.569	8.755	8.940	9.126	9.311	9.496	8.662	0.315
0.8	7.066	7.313	7.560	7.807	8.054	8.302	8.549	8.796	9.043	9.290	8.178	0.560
1.0	6.303	6.612	6.921	7.230	7.539	7.848	8.157	8.466	8.775	9.084	7.694	0.875
1.2	5.541	5.912	6.283	6.654	7.024	7.395	7.766	8.137	8.508	8.878	7.210	1.260
1.4	4.779	5.212	5.644	6.077	6.509	6.942	7.375	7.807	8.240	8.672	6.726	1.715
1.6	4.017	4.511	5.006	5.500	5.994	6.489	6.983	7.478	7.972	8.466	6.242	2.241
1.8	3.255	3.811	4.367	4.923	5.479	6.036	6.592	7.148	7.704	8.260	5.757	2.836
2.0	2.492	3.110	3.728	4.346	4.964	5.582	6.200	6.818	7.436	8.054	5.273	3.501
2.2	1.730	2.410	3.090	3.770	4.449	5.129	5.809	6.489	7.169	7.848	4.789	4.236
2.4	0.968	1.710	2.451	3.193	3.934	4.676	5.418	6.159	6.901	7.642	4.305	5.041
2.6	0.206	1.009	1.813	2.616	3.419	4.223	5.026	5.830	6.633	7.436	3.821	5.917
2.8	-0.556	0.309	1.174	2.039	2.904	3.770	4.635	5.500	6.365	7.230	3.337	6.862
3.0	-1.319	-0.392	0.535	1.462	2.389	3.316	4.243	5.170	6.097	7.024	2.853	7.877
3.2	-2.081	-1.092	-0.103	0.886	1.874	2.863	3.852	4.841	5.830	6.818	2.369	8.962
3.4	-2.843	-1.792	-0.742	0.309	1.359	2.410	3.461	4.511	5.562	6.612	1.885	10.118
3.6	-3.605	-2.493	-1.380	-0.268	0.844	1.957	3.069	4.182	5.294	6.406	1.401	11.343
3.8	-4.367	-3.193	-2.019	-0.845	0.329	1.504	2.678	3.852	5.026	6.200	0.916	12.639
4.0	-5.130	-3.894	-2.658	-1.422	-0.186	1.050	2.286	3.522	4.758	5.994	0.432	14.004
期望	2.111	2.760	3.409	4.058	4.707	5.356	6.005	6.654	7.302	7.951
方差	20.333	17.170	14.273	11.644	9.283	7.189	5.362	3.802	2.510	1.485

$ε$	$M$										期望	方差
$ε$	0.3	0.6	0.9	1.2	1.5	1.8	2.1	2.4	2.7	3.0	期望	方差
0.2	0.039	0.057	0.073	0.085	0.093	0.097	0.095	0.087	0.071	0.049	0.075	0.000
0.4	0.039	0.057	0.073	0.085	0.094	0.099	0.099	0.094	0.083	0.065	0.079	0.000
0.6	0.039	0.057	0.073	0.085	0.095	0.101	0.103	0.100	0.093	0.080	0.082	0.000
0.8	0.039	0.057	0.073	0.086	0.096	0.103	0.106	0.106	0.101	0.092	0.086	0.001
1.0	0.039	0.057	0.072	0.086	0.096	0.104	0.109	0.111	0.109	0.103	0.089	0.001
1.2	0.038	0.056	0.072	0.086	0.097	0.106	0.112	0.115	0.115	0.112	0.091	0.001
1.4	0.038	0.056	0.072	0.086	0.098	0.107	0.114	0.118	0.120	0.119	0.093	0.001
1.6	0.038	0.056	0.072	0.086	0.098	0.108	0.116	0.122	0.125	0.125	0.095	0.001
1.8	0.038	0.056	0.072	0.087	0.099	0.110	0.118	0.124	0.128	0.130	0.096	0.001
2.0	0.037	0.056	0.072	0.087	0.100	0.111	0.119	0.127	0.132	0.135	0.097	0.001
2.2	0.037	0.055	0.072	0.087	0.100	0.111	0.121	0.128	0.134	0.138	0.098	0.001
2.4	0.037	0.055	0.072	0.087	0.101	0.112	0.122	0.130	0.136	0.140	0.099	0.001
2.6	0.036	0.055	0.072	0.087	0.101	0.113	0.123	0.131	0.138	0.142	0.100	0.001
2.8	0.036	0.054	0.072	0.087	0.101	0.114	0.124	0.132	0.139	0.144	0.100	0.001
3.0	0.035	0.054	0.072	0.088	0.102	0.114	0.125	0.133	0.140	0.145	0.101	0.001
3.2	0.035	0.054	0.072	0.088	0.102	0.115	0.125	0.134	0.141	0.146	0.101	0.001
3.4	0.034	0.053	0.071	0.088	0.102	0.115	0.126	0.135	0.142	0.147	0.101	0.002
3.6	0.033	0.053	0.071	0.088	0.103	0.116	0.126	0.135	0.142	0.148	0.102	0.002
3.8	0.033	0.053	0.071	0.088	0.103	0.116	0.127	0.136	0.143	0.148	0.102	0.002
4.0	0.032	0.052	0.071	0.088	0.103	0.116	0.127	0.136	0.143	0.148	0.102	0.002
期望	0.037	0.055	0.072	0.087	0.099	0.109	0.117	0.122	0.124	0.123
方差	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.001

$ε$	$M$										期望	方差
$ε$	0.3	0.6	0.9	1.2	1.5	1.8	2.1	2.4	2.7	3.0	期望	方差
0.2	0.064	0.064	0.065	0.065	0.065	0.065	0.065	0.065	0.065	0.064	0.065	0.000
0.4	0.049	0.053	0.056	0.059	0.061	0.062	0.064	0.064	0.065	0.065	0.060	0.000
0.6	0.011	0.023	0.033	0.041	0.048	0.054	0.058	0.061	0.063	0.065	0.046	0.000
0.8	-0.057	-0.031	-0.009	0.010	0.025	0.038	0.048	0.055	0.060	0.063	0.020	0.002
1.0	-0.163	-0.115	-0.074	-0.039	-0.011	0.013	0.031	0.045	0.054	0.061	-0.020	0.006
1.2	-0.311	-0.233	-0.166	-0.109	-0.062	-0.023	0.007	0.030	0.046	0.057	-0.076	0.016
1.4	-0.501	-0.388	-0.288	-0.202	-0.130	-0.072	-0.025	0.010	0.035	0.052	-0.151	0.036
1.6	-0.732	-0.578	-0.440	-0.320	-0.217	-0.133	-0.067	-0.016	0.020	0.045	-0.244	0.072
1.8	-0.994	-0.800	-0.622	-0.462	-0.324	-0.210	-0.118	-0.048	0.002	0.036	-0.354	0.127
2.0	-1.277	-1.046	-0.828	-0.628	-0.451	-0.301	-0.180	-0.087	-0.020	0.025	-0.479	0.205
2.2	-1.566	-1.308	-1.054	-0.814	-0.597	-0.409	-0.254	-0.133	-0.046	0.013	-0.617	0.305
2.4	-1.852	-1.574	-1.292	-1.016	-0.759	-0.530	-0.339	-0.187	-0.077	-0.002	-0.763	0.423
2.6	-2.123	-1.837	-1.535	-1.230	-0.935	-0.666	-0.435	-0.250	-0.112	-0.020	-0.914	0.553
2.8	-2.374	-2.087	-1.776	-1.449	-1.122	-0.814	-0.542	-0.320	-0.153	-0.039	-1.068	0.688
3.0	-2.603	-2.322	-2.008	-1.668	-1.316	-0.972	-0.660	-0.398	-0.198	-0.062	-1.221	0.821
3.2	-2.810	-2.537	-2.227	-1.882	-1.512	-1.137	-0.786	-0.484	-0.250	-0.087	-1.371	0.948
3.4	-3.000	-2.734	-2.432	-2.087	-1.706	-1.308	-0.921	-0.578	-0.306	-0.115	-1.519	1.066
3.6	-3.174	-2.915	-2.620	-2.282	-1.897	-1.480	-1.061	-0.679	-0.368	-0.146	-1.662	1.172
3.8	-3.339	-3.081	-2.794	-2.463	-2.080	-1.652	-1.207	-0.786	-0.435	-0.180	-1.802	1.267
4.0	-3.497	-3.238	-2.955	-2.632	-2.255	-1.821	-1.355	-0.899	-0.507	-0.217	-1.938	1.353
期望	-1.512	-1.334	-1.148	-0.955	-0.759	-0.565	-0.384	-0.227	-0.103	-0.016	-0.700
方差	1.628	1.389	1.138	0.879	0.625	0.396	0.216	0.096	0.033	0.008	0.475

References 31

1	人民日报.碳排放配额累计成交量1.79亿吨［EB/OL］.（2022-01-04）［2022-01-04］..
	Daily People’s. The cumulative trading volume of carbon emission quotas reached 179 million tons（2022-01-04）［2022-01-04］..
2	黄晟，王静宇，李振宇. 碳中和目标下石油与化学工业绿色低碳发展路径分析［J］. 化工进展， 2022， 41（4）： 1689-1703.
	Huang S， Wang J Y， Li Z Y. Analysis of green and low-carbon development path of petroleum and chemical industry under the goal of carbon neutrality［J］. Chemical Industry and Engineering Progress， 2022， 41（4）： 1689-1703.
3	Wright C. Global banks， the environment， and human rights： The impact of the equator principles on lending policies and practices［J］. Global Environmental Politics， 2012，12（1）： 56-77.
4	Thompson P. Bank lending and the environment： Policies and opportunities［J］. International Journal of Bank Marketing， 1998，16（6）： 243-252.
5	Sarokin D， Schulkin J. Environmental concerns and the business of banking［J］. Journal of Commercial Bank Lending， 1991， 74（5）： 6-19.
6	Smith D R. Environmental risk： Credit approaches and opportunities： An interim report Z［R］. Working Paper， McGraw Hill， Geneva，United Nations Environment Programme， 1994.
7	丁宁，任亦侬，左颖. 绿色信贷政策得不偿失还是得偿所愿？——基于资源配置视角的PSM-DID 成本效率分析［J］. 金融研究， 2020（4）： 112-130.
	Ding N， Ren Y N， Zuo Y. Do the losses of the green-credit policy outweigh the gains？ A PSM-DID cost-efficiency analysis based on resource allocation［J］. Journal of Financial Research， 2020（4）： 112-130.
8	王修华，刘锦华，赵亚雄. 绿色金融改革创新试验区的成效测度［J］. 数量经济技术经济研究， 2021， 38（10）： 107-127.
	Wang X H， Liu J H， Zhao Y X. Effectiveness measurement of green finance reform and innovation pilot zone［J］. The Journal of Quantitative & Technical Economics， 2021， 38（10）： 107-127.
9	王馨，王营. 绿色信贷政策增进绿色创新研究［J］. 管理世界， 2021， 37（6）： 173-188+11.
	Wang X， Wang Y. Research on the green innovation promoted by green credit policies［J］.Journal of Management World， 2021， 37（6）： 173-188+11.
10	苏冬蔚，连莉莉. 绿色信贷是否影响重污染企业的投融资行为？［J］. 金融研究， 2018， 462（12）： 123-137.
	Su D W， Lian L L. Does green credit policy affect corporate financing and investment？ Evidence from publicly listed firms in pollution － intensive industries？［J］. Journal of Financial Research， 2018， 462（12）： 123-137.
11	牛海鹏，张夏羿，张平淡. 我国绿色金融政策的制度变迁与效果评价——以绿色信贷的实证研究为例［J］. 管理评论， 2020， 32（8）： 3-12.
	Niu H P， Zhang X Y， Zhang P D. Institutional change and effect evaluation of green finance policy in china： Evidence from green credit policy［J］. Management Review， 2020， 32（8）： 3-12.
12	张令荣，杨子凡，程春琪. 碳配额交易政策下闭环供应链的减排策略选择［J］. 管理工程学报， 2022， 36（1）： 172-180.
	Zhang L R， Yang Z F， Cheng C Q. Selection of emission reduction strategy for closed-loop supply chain under carbon cap-and-trade policy［J］. Journal of Industrial Engineering and Engineering Management， 2022， 36（1）： 172-180.
13	Benjaafar S， Li Y， Daskin M. Carbon footprint and the management of supply chains： Insights from simple models［J］. IEEE Transactions on Automation Science and Engineering， 2012， 10（1）：99-116.
14	Xu X P， Zhang W， He P， et al. Production and pricing problems in make-to-order supply chain with cap-and-trade regulation［J］.Omega，2017， 66： 248-257.
15	Kuiti M R， Ghosh D， Basu P， et al. Do cap-and-trade policies drive environmental and social goals in supply chains： Strategic decisions， collaboration， and contract choices［J］. International Journal of Production Economics， 2020， 223： 107537.
16	马秋卓，宋海清，陈功玉. 碳配额交易体系下企业低碳产品定价及最优碳排放策略［J］. 管理工程学报， 2014， 28（2）： 127-136.
	Ma Q Z， Song H Q， Chen G Y. A study on low-carbon product pricing and carbon emission problems under the cap-and-trade system［J］. Journal of Industrial Engineering and Engineering Management， 2014， 28（2）： 127-136.
17	夏良杰，孔清逸，李友东，等. 考虑交叉持股的低碳供应链减排与定价决策研究［J］. 中国管理科学， 2021， 29（4）： 70-81.
	Xiang L J， Kong Q Y， Li Y D， et al. Emission reduction and pricing strategies of a low-carbon supply chain considering cross-shareholding［J］. Chinese Journal of Management Science， 2021， 29（4）： 70-81.
18	方国昌，何宇，田立新. 碳交易驱动下的政企碳减排演化博弈分析［J］. 中国管理科学， 2024，32（5）： 196-206.
	Fang G C， He Y， Tian L X. Evolutionary game analysis of government and enterprises carbon-reduction under the driven of carbon trading［J］. Chinese Journal of Management Science， 2024，32（5）： 196-206.
19	周永圣，刘巧荣，李健，等.基于绿色信贷的政府促进银行实施代理监督权的博弈研究［J］. 系统工程理论与实践， 2015， 35（7）： 1744-1751.
	Zhou Y S， Liu Q R， Li J， et al. Game model for governments to promote banks as the agency to supervise the implementation of green supply chain based on green credit［J］. Systems Engineering-Theory & Practice， 2015， 35（7）： 1744-1751.
20	张维迎. 博弈论与信息经济学［M］. 上海：上海人民出版社， 2004.
	Zhang W Y. Game theory and information economics［M］.Shanghai： Shanghai People's Publishing House， 2004.
21	Ding H P， Zhao Q L， An Z R， et al. Collaborative mechanism of a sustainable supply chain with environmental constraints and carbon caps［J］. International Journal of Production Economics， 2016， 181： 191-207.
22	范定祥，李重莲，王晓蕾.基于Stackelberg博弈的闭环供应链回收模式选择及其优化研究［J］.运筹与管理，2021，30（4）：135-141.
	Fan D X， Li C L， Wang X L. Research on selection and optimization of closed-loop supply chain recycling mode based on stackelberg game［J］. Operations Research and Management Science， 2021， 30（4）： 135-141.
23	代应，戴佳宏，宋寒，等. 碳交易下驱动企业碳减排的碳配额机制［J］.计算机集成制造系统，2024， 30（4）： 1518-1526.
	Dai Y， Dai J H， Song H， et al. Carbon quota mechanism to drive carbon emission reduction of enterprises under carbon trading［J］. Computer Integrated Manufacturing Systems， 2024， 30（4）： 1518-1526.
24	Kouvelis P， Zhao W H. Who should finance the supply chain？ Impact of credit ratings on supply chain decisions［J］. Manufacturing & Service Operations Management， 2017， 20（1）： 19-35.
25	Lu X， Shang J， Wu S Y，et al. Impacts of supplier hubris on inventory decisions and green manufacturing endeavors［J］.European Journal of Operational Research， 2015， 245（1）：121-132.
26	夏良杰，曹云丽，尹文昊，等. 碳交易规制下过度自信制造商与减排服务商的减排博弈研究［J］. 中国管理科学， 2023，31（10）： 85-95.
	Xia L J， Cao Y L， Yin W H， et al. Research on emission reduction game between an overconfident manufacturer and an emission reduction service supplier under the cap-and-trade regulation［J］. Chinese Journal of Management Science， 2023，31（10）： 85-95.
27	Du S F， Hu L， Song M L. Production optimization considering environmental performance and preference in the cap-and-trade system［J］. Journal of Cleaner Production， 2016， 112： 1600-1607.
28	Xia L J， Bai Y W， Ghose S， et al. Differential game analysis of carbon emissions reduction and promotion in a sustainable supply chain considering social preferences［J］. Annals of Operations Research， 2022， 310（1）： 257-292.
29	Hong Z F， Chu C P， Zhang L L， et al. Optimizing an emission trading scheme for local governments： A Stackelberg game model and hybrid algorithm［J］. International Journal of Production Economics， 2017， 193： 172-182.
30	Cheng Y X， Wu D D， Olson D L， et al. Financing the newsvendor with preferential credit： Bank vs. manufacturer［J］. International Journal of Production Research， 2021， 59（14）： 4228-4247.
31	姚凡军，肖汉，高野，等. 碳配额交易政策下考虑消费者偏好和成员行为的供应链微分博弈研究［J］. 中国管理科学， 2024， 32（1）： 231-241.
	Yao F J， Xiao H， Gao Y， et al. A differential game study of supply chain considering consumers’ green preference and member’s behavior under cap-and-trade policy［J］. Chinese Journal of Management Science， 2024， 32（1）： 231-241.

[1]	Lili Ding, Zhongchao Zhao, Kaixuan Zhang. Research on the Mechanism of Perceived Value on the Development of Personal Carbon Account Green Credit [J]. Chinese Journal of Management Science, 2025, 33(5): 344-355.
[2]	Haojia He,Xingzheng Ai,Hua Tang,Songbo Guo. Patent Licensing Strategy Selection in the Presence of Chain-to-Chain Competition [J]. Chinese Journal of Management Science, 2024, 32(9): 142-153.
[3]	Nan Xie,Haitao He,Yanju Zhou,Zongrun Wang. Research on Supply Chain Financial Decision Based on the Analysis of Central Government Project Subsidy under the Background of Rural Revitalization Subsidy [J]. Chinese Journal of Management Science, 2024, 32(8): 214-229.
[4]	Guochang Fang,Yu He,Lixin Tian. Evolutionary Game Analysis of Government and Enterprises Carbon-reduction under the Driven of Carbon Trading [J]. Chinese Journal of Management Science, 2024, 32(5): 196-206.
[5]	Yanan Song,Ruijuan Nan,Wei Gu,Daoping Wang. Research on Pricing and Subsidy Strategies of Ride-sharing Platform Based on Driver's Service Effort level [J]. Chinese Journal of Management Science, 2024, 32(2): 265-275.
[6]	Xin Cui,Chunfa Li,Chi Zhou,Xinxin Mi,Yao Shen. Logistics Cooperation and Operations Decision of fresh E-commerce Supply Chain Considering Random Demand [J]. Chinese Journal of Management Science, 2024, 32(2): 87-98.
[7]	Jiayu Zheng, Yi Hu. Can Green Credit Drive the “Greening” of the Financial System and Enterprise Emission Reduction?Based on Evolutionary Game Analysis [J]. Chinese Journal of Management Science, 2024, 32(12): 288-299.
[8]	Baichen Xie, Lu Zhang, Peng Hao. Real-time Pricing Demand Response Mechanism of Virtual Power Plant Based on Stackelberg Game [J]. Chinese Journal of Management Science, 2024, 32(12): 312-322.
[9]	Bingsheng Liu, Qiaoyan Cui, Dan Wang, Jianfeng Zhao. A Dynamic Game Model for Post-disaster Resilience Governance of Transport Infrastructure Based on Public Participation [J]. Chinese Journal of Management Science, 2024, 32(11): 189-200.
[10]	Ai-hong LIAO,Yan-qiu LU,Li-jun PANG. Research on the Selection of Government R&D Subsidy Strategy under Leading Enterprise-driven Cluster [J]. Chinese Journal of Management Science, 2023, 31(8): 22-31.
[11]	XIA Xi-qiang, LU Meng-yuan, LI Biao. Study on the Impact of Carbon Trading on Remanufacturing under Intellectual Property Protection [J]. Chinese Journal of Management Science, 2023, 31(7): 115-125.
[12]	FENG Chun, HE Zheng, GUO Qian-yun, ZHANG Yi. Strategies of Retailer Introducing Poverty Alleviation Products [J]. Chinese Journal of Management Science, 2023, 31(6): 25-38.
[13]	LIU Bi-yu, YANG Hai-dong, KE Di. Effects of Carbon Emission Regulations on Operations Decisions of Manufacturers/Remanufacturers with Patent Licensing [J]. Chinese Journal of Management Science, 2023, 31(5): 198-208.
[14]	Chun FENG,Qian-yun GUO. Research on the Quality Positioning of Poverty Alleviation Agricultural Products under Consumers' Perception [J]. Chinese Journal of Management Science, 2023, 31(11): 103-113.
[15]	Xiu-mei WANG,Jun LI. Strategies for Powerful Retailers' Gray Market Channel Structure Selection [J]. Chinese Journal of Management Science, 2023, 31(11): 248-257.

Low-carbon Behavior Decision of Chemical Enterprises Based on Carbon Trading and Green Credit

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 15

References 31

Related Articles 15

Metrics

Comments

Recommended 0