Frontiers in Business, Economics and Management 
ISSN: 2766-824X | Vol. 17, No. 2, 2024 

 

54 

Has Green Insurance Ensured the Realization of China 
's Dual Carbon Goals? 

Ke Yan * 

School of Economics and Management, Xinjiang University, Wulumuqi, 830046, China 

* Corresponding author: 13363043178@163.com 

 

Abstract: As an essential risk guarantee and economic compensation mechanism to construct green and low-carbon circular 
economic development system, the role of green insurance in the field of energy conservation and emission reduction remains a 

realm yet to be explored substantially. Based on the panel data of 30 provinces in China from 2005 to 2019, the influence and 

theoretical mechanism of green insurance on carbon emissions were analyzed by the construction of intermediary effect model 

and moderating effect model. Additionally, the spatial spillover effect was explored by the spatial Durbin model. The results 

show that: (1) Green insurance has a significant inhibitory effect on carbon emissions, which retains salience across the spectrum 

of endogenous tests and stability tests. (2) Both the “local effect” and “neighboring effect” of green insurance on carbon 

emissions are inhibitory, displaying “simultaneous resonance” characteristics based on the spatial perspective. In view of the 

above conclusions, this thesis provides policy suggestions for promoting the construction of a diversified and collaborative green 

insurance system and enriching carbon emission reduction paths. 

Keywords: Green insurance, Carbon emissions, Spatial spillover effect. 

 

1. Introduction 

The chain reaction caused by the intensified greenhouse 

effect has induced a series of ecological problems, such as the 

glacier retreat and the northward migration of the climate 

zone, which has more seriously restricted the healthy and 

sustainable development of China's economy [1-3]. The 

report of the 20th National Congress of the Communist Party 

of China has repeatedly underscored the goal of 'carbon peak 

and carbon neutrality', withal, it said that it is necessary to 

actively and steadily promote carbon peak and carbon 

neutrality, especially in the dual control of dual control of 

carbon emissions, energy revolution, sound carbon market, 

and improvement of carbon sequestration capacity. Hence, it 

is imperative to establish and improve the economic system 

of green low-carbon cycle development and explore and 

improve the path of carbon emission reduction. 

Green insurance is the basic means of environmental risk 

management under the condition of market economy, 

including environmental pollution liability insurance. Green 

insurance gives full play to the function of strict control and 

sharing of environmental risks in promoting the prevention 

and control of environmental pollution and improving the 

modern environmental governance system. Green insurance 

guarantees the development of green low-carbon industries 

and improves the market-oriented allocation system of 

resource and environmental factors. Concurrently, green 

insurance accelerates the development and application of 

advanced technologies for energy conservation and carbon 

reduction, promoting the formation of green and low-carbon 

production methods and lifestyles [4]. Therefore green 

insurance has a positive effect on the achievement of the dual 

carbon goal. 

This study tries to solve the following questions: First, can 

green insurance promote carbon emission reduction? Second, 

does green insurance have a spatial spillover effect on carbon 

emission reduction? 

The potential research value of this thesis is reflected in the 

following aspects. On the one hand, energy consumption 

structure, environmental regulation and energy intensity are 

taken as the intermediary variables to explore the channels of 

green insurance to reduce carbon emission, which provides 

paths for how to promote the carbon emission reduction effect. 

On the other hand, this dissertation adopts the spatial Durbin 

model to further explore the spatial spillover effect of green 

insurance on carbon emissions to make up for the lack of 

research on the spatial spillover effect of green insurance. 

2. Literature Review 

Research on carbon emissions by various green policies has 

emerged in an endless stream and the research on carbon 

emissions by green finance [5-7], green credit [8-10] and 

carbon emission pilot policies [11-13] has been continuously 

enriched and in-depth, however, the relative research on 

carbon emissions of green insurance is quite rare. 

Such studies mainly focus on pollution emissions and 

agricultural carbon emissions. Shi et al. employed the DID 

method to empirically test that the EPLI policy is a market 

tool to reduce the risk of industrial pollution [14]. Focusing 

on the agricultural industry, Ma and Cui found that the 

development of agricultural insurance significantly reduced 

the total amount, density and intensity of agricultural carbon 

emissions from three perspectives: development scale, 

coverage density and coverage breadth [15]. The above 

articles have made vital contributions to the research on the 

connotation, function and mechanism of green insurance, 

which provides important ideas for this paper. However, such 

articles have certain limitations in the research object along 

with dimension. The research object is industrial SO2 and 

dust emissions so that the correlation between green insurance 

and carbon emissions has not been directly explored. 

Concurrently, the agricultural industry, as the main research 

dimension, has certain industry limitations. On top of that, 

such articles do not take geographical factors into account, 

which ignores the spatial spillover effect of green insurance. 



 

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Collectively, in view of the lack of relevant research, it is 

of great theoretical and practical significance to empirically 

study the impact mechanism of green insurance on carbon 

emissions. With the object of further promoting the internal 

mechanism of green insurance and carbon emissions, this 

dissertation explores the channels of carbon emission 

reduction in green insurance through energy consumption 

structure, environmental regulation and energy consumption 

intensity. Additionally, the moderating effects of urban-rural 

income gap, high-quality economic development and 

industrial structure are studied. Finally, this paper employs the 

spatial Durbin model to study the spatial spillover effect of 

green insurance on carbon emissions, which makes up for the 

lack of spatial research on green insurance and provides 

specific policy recommendations on how to promote the 

development of green insurance and carbon emission 

reduction based on the empirical results. 

3. Methodology and Data 

3.1. Model Specification 

3.1.1. Benchmark model 
To explore the impact of green insurance on carbon 

emissions, this article establishes a regression model: 

Carboni,t  = β0 +  β1GIi,t + βsCVsi,t + μi + γt + εi,t  (1) 

Where i represents the province and t represents the year, 

Carboni,t  represents the CO2 emissions in t in province i, 

GIi,t represents the green insurance in t in province i, β1is the 
coefficient of green insurance, which represents the degree of 

influence of green insurance on carbon emissions, CVsi,t is a 

series of control variables, βs  is the corresponding 
coefficient of each control variable, μi  represents the fixed 
effect of province, γt represents the fixed effect of time.εi,t 
is the random error term. 

3.1.2. Spatial Durbin model 
When insurance companies launch relevant green 

insurance products, the surrounding insurance companies will 

also launch them to meet the market demand. At the same 

time, the surrounding areas will also follow the example of 

the region to make relevant policy adjustments and ultimately 

achieve the reduction of carbon emissions. In an attempt to 

study the spatial spillover effect of green insurance on carbon 

emissions, this essay constructs the following spatial Durbin 

model. 

Carboni,t = ρ ∑ wi,jCarboni,t
N
j=1 + αGIi,t +

α1 ∑ wi,jGIi,t
N
j=1 + βsCVsi,t + ϑs ∑ wi,jCVsi,t

N
j=1 + μi +  γi +

εi,t (2) 

Equation (2) is the spatial Durbin model, and ρ  is the 
spatial autoregression coefficient, which represents the 

degree of influence of the carbon emissions of neighboring 

provinces on the carbon emissions of the province.α1 is the 
lag coefficient of green insurance, which represents the 

degree of the impact of green insurance in the pilot areas on 

carbon emissions in the surrounding areas.βs is the degree of 
the influence of the control variable on the carbon emissions 

of the province.ϑs is the degree to which the control variable 
affects the carbon emissions of the neighboring province.wi,j 

is the basic element of the spatial weight matrix. 

3.2. Sample Selection and Data Sources 

From the statistical and data rationality point of view, Hong 

Kong, Macao and Taiwan and the Tibet Autonomous Region, 

respectively, due to the lack of data, this article does not 

include them in the scope of this measure. On account of 

missing values in some data in individual years, this article 

uses linear interpolation method for estimation. Referring to 

previous studies, carbon emissions are measured by sectoral 

methods and are derived from the China Carbon Accounting 

Database (CEADs). The data of explanatory variables and 

control variables are from the National Bureau of Statistics, 

China Statistical Yearbook and China Industrial Statistical 

Yearbook. 

4. Analysis of Empirical Results 

4.1. Descriptive Statistics of The Variables 

Data from 30 provinces in China from 2005 to 2019 were 

selected for analysis and 450 total sets of data were obtained. 

The descriptions of the variables used in this article are shown 

in Table 1. 

 

Table 1. Descriptive statistics of variables 

Variable N Mean p50 SD Min Max Range 

GI 450 0.733 0.449 0.991 0 7.972 7.972 

Carbon 450 29.39 23.36 20.01 1.646 93.71 92.06 

P 450 4.489 2.831 6.622 0.0750 39.13 39.05 

lnpatent 450 9.371 9.425 1.639 4.369 13.18 8.806 

C 450 0.0430 0.0210 0.0460 0.00200 0.230 0.228 

Pgdp 450 4.496 3.830 2.747 0.543 12.49 11.95 

Ec 450 11.31 7.146 10.22 0.0490 43.30 43.26 

P1 450 4.496 3.830 2.747 0.543 12.49 11.95 

GC 450 0.542 0.536 0.144 0.192 0.906 0.714 

GS 450 0.224 0.185 0.179 0.00100 0.893 0.892 

GIV 450 0.00100 0.00100 0.00100 0 0.0100 0.0100 

ES 450 0.950 0.881 0.411 0.0250 2.461 2.436 

4.2. Benchmark Regression 

Table 2 reports the test results of the benchmark regression. 

Based on the two-way fixed effect model, columns (1) and (2) 

respectively reported the regression results of whether to add 

the control variable or not, and the coefficient was 

significantly negative at the level of 1%, which indicated that 

GI has a significant inhibitory effect on carbon emissions, and 

the hypothesis H1 was verified. 

 

 



 

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Table 2. Benchmark regression results 

 (1) (2) 

 Carbon Carbon 

GI -28.296*** -42.520*** 

 (6.794) (8.763) 

P  -1.796*** 

  (0.373) 

lnpatent  0.061 

  (0.867) 

C  -75.670*** 

  (20.605) 

Pgdp  4.705*** 

  (1.060) 

Ec  0.014 

  (0.027) 

_cons 21.208*** 12.428 

 (1.028) (8.583) 

province Yes Yes 

year Yes Yes 

F 46.641 43.275 

R2 0.633 0.684 

N 450 450 

Note: Standard errors in parentheses * p < 0.1, ** p < 0.05, *** 

p < 0.01, and the same below. 

4.3. Analysis of the Spatial Durbin Model 
Regression 

The data in this article are tested by LM, LR and Wald. The 

results of Hausman test show that the fixed effect model is 

more accurate. In this paper, the spatial Durbin model with 

double fixed effects is used. Based on the three matrices of 

adjacency matrix, economic distance matrix and linear 

geographic matrix, this paper adopts the spatial Durbin model 

for regression and the regression results are still significant. 

According to Table 3, the spatial lag term and each 

explanatory variable passed the significance level test. 

 

Table 3. Results of the spatial Durbin model regression 

 (1) (2) (3) 

 W1 W2 W3 

GI -65.363*** -71.033*** -45.761*** 

 (0.00) (0.00) (0.00) 

W*GI -30.969* -85.368* 71.912 

 (0.09) (0.06) (0.12) 

CVS Yes Yes Yes 

Year Yes Yes Yes 

Province Yes Yes Yes 

N 450 450 450 

R2 0.380 0.327 0.223 

Number of id 30 30 30 

 

Green insurance can significantly reduce carbon emissions 

in adjacent areas and close economic distances. Additionally, 

green insurance has a stronger inhibitory effect on carbon 

emissions in areas with closer economic distances. The spatial 

effect of green insurance in the linear distance matrix is not 

obvious, which is closely related to the immature 

development of green insurance. 

5. Conclusion and Revelation 

5.1. Conclusion 

Based on the panel data of 30 provinces in China from 2005 

to 2019, this article empirically illuminates carbon emission 

reduction effect of role of green insurance’s evolution in the 

context of engendering ecological stewardship within an 

actualised economy at a granular level. In addition, it utilizes 

the spatial Durbin model to further explore the spatial 

spillover effect of green insurance on carbon emissions. The 

conclusions are as follows: 

First, green insurance has a significant inhibitory effect on 

carbon emissions and the research conclusion retains salience 

across an array of endogenous tests and robustness tests. 

Second, green insurance has a significantly negative spatial 

spillover effect on carbon emissions in adjacent regions. 

Further research finds that compared with the spatial spillover 

effect of adjacent regions, regions with similar economic 

distances have a stronger spatial spillover effect on the region. 

5.2. Revelation 

In order to promote the achievement of the two-carbon 

target, based on the perspective of green insurance, this 

dissertation makes the following suggestions: 

Insurance companies should innovatively develop green 

insurance products and expand the coverage of green 

insurance to build a multi-coordinated green insurance system 

and enrich the green development mode. Green insurance has 

a significant inhibitory effect on carbon emissions. 

Consequently, insurance companies necessitate active 

cooperation with the pilot work of environmental pollution 

liability insurance and strengthen their own responsibilities 

and obligations. Simultaneously, it is not limited to state-

owned enterprises, expanding the scope of underwriting and 

expanding the coverage of green insurance. It is suggested 

that insurance companies should focus on the new era and 

keep pace with the times. While updating and improving the 

relevant traditional green insurance, insurance companies 

should continue to design and promote diversified new green 

insurance products. Product design should effectively 

improve the ability of enterprise risk prevention, control and 

resolution, thereby providing risk guarantee for the 

achievement of dual carbon goals. 

References 

[1] Lin B, Jia Z. What will China's carbon emission trading market 
affect with only electricity sector involvement? A CGE based 
study [J]. Energy Economics, 2019, 78: 301-311. 

[2] Shuai C, Chen X, Shen L, et al. The turning points of carbon 
Kuznets curve: evidences from panel and time-series data of 
164 countries [J]. Journal of cleaner production, 2017, 162: 
1031-1047. 

[3] Villoria-Sáez P, Tam V W Y, del Río Merino M, et al. 
Effectiveness of greenhouse-gas Emission Trading Schemes 
implementation: a review on legislations [J]. Journal of cleaner 
production, 2016, 127: 49-58. 

[4] Chen J. Development of green insurance ideas and 
countermeasures [J]. Southern Finance, 2016 (9): 14-17. 

[5] Jiang H, Wang W, Wang L, Wu J. Research on the effect of 
carbon emission reduction in the development of green finance 
in China-Taking green credit and green venture capital as 
examples [J]. Financial Forum, 2020, 25 (11): 11. 



 

57 

[6] Hu Y, Du S, Wang Y, et al. How Does Green Insurance Affect 
Green Innovation? Evidence from China [J]. Sustainability, 
2023, 15(16): 12194. 

[7] Ran Q, Liu L, Razzaq A, et al. Does green finance improve 
carbon emission efficiency? Experimental evidence from 
China [J]. Environmental science and pollution research, 2023, 
30(16): 48288-48299. 

[8] Yin H, Wang L, Liu N. Green credit and carbon emissions: 
emission reduction effect and transmission path [J]. 
Environmental science and management, 2019, 44 (11): 6. DOI: 
CNKI: SUN: BFHJ.0.2019-11-003. 

[9] Xu P, Ye P, Jahanger A, et al. Can green credit policy reduce 
corporate carbon emission intensity: Evidence from China's 
listed firms [J]. Corporate Social Responsibility and 
Environmental Management, 2023. 

[10] Zhang A, Deng R, Wu Y. Does the green credit policy reduce 
the carbon emission intensity of heavily polluting industries?-
Evidence from China's industrial sectors [J]. Journal of 
Environmental Management, 2022, 311: 114815. 

[11] Zhang H. Can low-carbon city pilot policies reduce carbon 
emissions? - Evidence from quasi-natural experiments [J]. 
Economic Management, 2020, 42 (6): 17. DOI: CNKI: SUN: 
JJGU.0.2020-06-003. 

[12] Pan A, Zhang W, Shi X, et al. Climate policy and low-carbon 
innovation: Evidence from low-carbon city pilots in China [J]. 
Energy Economics, 2022, 112: 106129. 

[13] Zeng S, Jin G, Tan K, et al. Can low-carbon city construction 
reduce carbon intensity? Empirical evidence from low-carbon 
city pilot policy in China [J]. Journal of Environmental 
Management, 2023, 332: 117363. 

[14] Shi B, Jiang L, Bao R, et al. The impact of insurance on 
pollution emissions: Evidence from China's environmental 
pollution liability insurance [J]. Economic Modelling, 2023, 
121: 106229. 

[15] Ma J, Cui H. The role of agricultural insurance development in 
carbon emission reduction: effects and mechanisms [J]. China's 
population, resources and environment, 2021, 31 (10): 79-89.