The impact of energy price on CO2 emissions in China: A spatial econometric analysis

Journal Pre-proof The impact of energy price on CO2 emissions in China: A spatial econometric analysis

Kunming Li, Liting Fang, Lerong He PII:

S0048-9697(19)35937-6

DOI:

https://doi.org/10.1016/j.scitotenv.2019.135942

Reference:

STOTEN 135942

To appear in:

Science of the Total Environment

Received date:

18 September 2019

Revised date:

28 November 2019

Accepted date:

3 December 2019

Please cite this article as: K. Li, L. Fang and L. He, The impact of energy price on CO2 emissions in China: A spatial econometric analysis, Science of the Total Environment (2018), https://doi.org/10.1016/j.scitotenv.2019.135942

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© 2018 Published by Elsevier.

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The Impact of Energy Price on CO2 Emissions in China: A Spatial Econometric Analysis Dr. Kunming Li College of Economics Fujian Agriculture and Forestry University Fuzhou, Fujian, 350002, China Email: [email protected]

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Dr. Liting Fang* School of Economics & Management Fuzhou University Fuzhou, Fujian, 350116, China Email: [email protected]

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Dr. Lerong He School of Business & Management College at Brockport State University of New York Brockport, NY, 14450, USA Email: [email protected]

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*Corresponding author contact: Dr. Fang Liting, Department of statistics, School of Economics & Management, Fuzhou University, No.2 Xue Yuan Road, University Town, Fuzhou City, Fujian Province, PR China. Email: [email protected]

Acknowledgments: The work is supported by grants from the National Natural Science Foundation of China (Grant Number: 71703025), MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Grant Number: 17YJC910004, 19YJC910002), and Fujian Agricultural and Forestry University Fund Project for Distinguished Young Scientific Research Talents Program (Grant Number: xjq201821).

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The Impact of Energy Price on CO2 Emissions in China: A Spatial Econometric Analysis Abstract

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Excessive greenhouse gas emissions pose a global environmental concern. This paper examines the impact of energy price on China’s CO2 emissions based on an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) structural human ecology model. We utilize Chinese provincial data from 2002 to 2016 and apply the spatial panel data techniques to explicitly consider spatial correlations and spillover effects between observations. Our results show that energy price has a significant negative impact on China’s CO2 emissions after controlling for other economic and energy market factors and spatial correlations of these variables. We identify a significant direct impact of the focal province’s price change on CO2 emissions and an indirect effect exerted by energy price changes in adjacent provinces. Such spatial spillover effects are also observed in other determinants identified in the extended STIRPAT model. We also document a path dependent pattern in China’s provincial CO2 emissions with last period CO2 emissions influencing the current CO2 emission levels. Our results show that the negative impact of energy price on CO2 emissions remains qualitatively the same after incorporating the influence of previous period CO2 emissions. Our results are also robust to the inclusion of lagged energy price measures, alternative spatial economic models, and alternative spatial weight matrices. Overall, our paper highlights the role of energy market in curbing CO2 emissions and promoting sustainable economic development. Keywords: Energy price; CO2 emissions; Spatial econometric; STIRPAT model; Assessment; China

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1. Introduction Greenhouse gases such as Carbon dioxide (CO2), methane, and nitrous oxide are essential in sustaining a habitable temperature for the plane. However, excessive emissions of greenhouse gases create the greenhouse effect warming the earth’s temperature, and resulting in global warming and climate changes with harmful

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ecological, physical and health impacts. CO2 as one of the main greenhouse gases

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accounts for approximately 80% of global greenhouse gases emissions. More than 90%

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of CO2 emissions from human sources are caused by the usage of fossil fuel as well

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as cement production. Direct human-induced impacts on forestry and other land use, such as through deforestation, agricultural land clearing and soil degradation, also

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affect CO2 emissions. The latest data indicate that global CO2 emissions have

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reached 36.15 billion tones in 2017, with China being the largest CO2 emitters whose

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2017 emissions reach 9.84 billion tones and account for 27.21% of the total level.1 To mitigate the adverse effect of excessive carbon emissions on global environment and economy, the United Nations Framework Convention on Climate Change has set a target in the Paris Agreement to restrict average warmings to 2 degrees Celsius above pre-industrial temperatures.2 An important step in achieving this ambitious goal is to accelerate actions and increase investments needed for reducing fossil fuel usage and limiting CO2 emissions of member countries so as to create a sustainable low carbon developmental path. China as one of the endorser of the Paris Agreement and the largest CO2 and 1

http://www.globalcarbonatlas.org/en/CO2-emissions

2

https://unfccc.int/process-and-meetings/the-paris-agreement/what-is-the-paris-agreement

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greenhouse emitter in the world, has set a carbon emission target to reduce its CO2 emissions per unit of GDP (Gross Domestic Product) by 60% to 65% from the 2005 level by 2035.3 Over these years, the Chinese government has engaged in sturdy efforts to control its fossil fuel consumption and to reduce carbon emissions of the nation. For example, greenhouse gas emissions and environmental protection are now

government performance (Pan, et al., 2019).

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included in the Chinese central government’s evaluation system for assessing local The National Development and Reform

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Commission launched a nationwide carbon emissions trading system in the power

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generation industry in December 2017. In addition, the Chinese State Council has

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setup special supervisory divisions and issued a series of environmental protection

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regulations and laws to provide environmental monitoring of regional and national

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development.4 In spite of strenuous attempts of the Chinese government, the nation’s large population, high energy consumption base and low energy consumption

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efficiency challenge its endeavor to achieve its sustainable economic development goal (Dhakal, 2009). Therefore, systematically examining key contributors of CO2 emissions in China will not only offer valuable theoretical insights and additional empirical evidence, but also provide important practical implications for facilitating a sustainable growth of China and world economy. The key objective of this paper is to examine the impact of energy price on CO2 emissions. We explore this research question by utilizing Chinese provincial data from 2002 to 2016 and explicitly considering intra-region spatial correlations and 3

http://www.china.org.cn/business/2018-03/28/content_50757398.htm

4

http://www.gov.cn/zhengce/2019-06/17/content_5401085.htm

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spillover effects of adjacent provinces. Based on the doctrine of the neoclassical economics theory (Ferguson, 1969), supply and demand conditions in the energy market are driving forces behind energy pricing, production, and consumption. Consequently, higher energy price will lead to lower energy consumption, and accordingly result in a reduction of CO2 emissions. In addition, higher energy price

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also reflects a degree of higher energy scarcity, thus encourages substitution of pricy energy sources for cheaper alternatives, and consequently influence energy supply.

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Unlike energy markets in developed economics where energy price is determined by

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the invisible hand of the market, the Chinese energy market is distorted and

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fragmented under the heavy influence of the government’s grabbing hand (Hsieh and

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Klenow, 2009). Local governments in China often purposefully keep energy price low

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to attract firms particularly those in manufacturing sectors with heavy energy needs to relocate to the region to support regional economic development and employment

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growth (Tao et al., 2009). The aim of this paper therefore is to examine how energy price may affect CO2 emissions and become a mitigating mechanism in an inefficient energy market as China. By analyzing the role of energy price in this unique energy market condition, our study thus augments prior empirical literature that has investigated the impact of energy demand and supply conditions on carbon emissions and environmental pollutions in advanced economies with efficient energy markets (e.g., Al-Mulali et al. 2015; Jebli et al., 2016) to provide evidence on how market inefficiency may alter motivation and decisions of economic actors, and consequently affect greenhouse gas emissions.

Journal Pre-proof We investigate this research question by applying a longitudinal data at the Chinese provincial level. The focus on regions is warranted because regional carbon emissions are closely linked to key structural factors such as local economic development level, industrial structure, technological level and population that also have the potential to impact supply and demand conditions in the energy market. This design is particularly valuable in the Chinese context owing to significant differences between Chinese regions in terms of culture, consumption habits, market development

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level, and stringency of law enforcement. Local Chinese governments also possess

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noticeable autonomy in designing their economic development paths to balance

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between economic development and environmental protection goals. Another benefit

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of utilizing regional data is to reduce the influence of national level confounding factors such as national policy, economic development, industry structure, energy

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infrastructure and technology that may generate measurement noises and estimation

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biases on factors influencing CO2 or carbon emissions. By holding country level confounding factors at constant, our study also supplements prior multi-country

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comparative studies that have examined determinants of CO2 emissions (e.g., Abdallh and Abugamos, 2017; Kais & Sami, 2016; Narayan & Narayan, 2010) to reveal the influence of energy price on CO2 emissions in China. To better integrate these regional factors in our analysis, we utilize an extended STIRPAT structural human ecology model. STIRPAT is an acronym of Stochastic Impacts by Regression on Population, Affluence and Technology, which refers to a statistical and conceptual model for assessing human impacts on the environment. This model has been widely adopted as an analytical tool by human ecology scholars to identify primary human drivers of environmental harm. We

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incorporate energy price as an additional influencer to others key factors including population, affluence, and technology identified in the traditional STIRPAT model of environmental impact (Dietz and Rosa, 1994; Fan et al., 2006). The explicit incorporation of energy price in this extended STIRPAT model enables our study to augment prior sustainability literature that has applied the STIRPAT model to examine

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the impact of population size (Li et al., 2012; Yu et al., 2018; Zhu & Peng, 2012), urbanization (Zhang & Lin, 2012), industry structure (Shuai et al., 2014; Zhou et al.,

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2013), economic growth (Wang, Li & Fang, 2016; Zhang & Zhang, 2018), and

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technology development (Zhang, et al., 2017; Wang, Zeng & Wu., 2016) on carbon

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emissions and other environmental consequences in China. The economic angle built

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on the human ecology perspective of the STIRPAT model also enables our study to

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supplement prior works examining determinants of CO2 emissions from the perspective of environmental science in the context of China (Pan et al., 2016; Zhang

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et al., 2013) and other nations (e.g., Vanneste et al., 2011; Castellani, et al., 2017; Cecchel et al., 2017) to highlight the important of energy market factors and human actions in affecting carbon emissions and contributing to sustainable development. Our paper also contributes to extant literature by incorporating a novel statistical technique, namely spatial econometric method in the analysis (Elhorst, 2010). Prior empirical studies on this research topic have typically applied multivariable regression methods such as pooled cross-sectional models, panel data methods, or time series models to conduct analysis. These models by default assume that dependent variables (in our case CO2 emissions) and explanatory variables (energy market factors and other determinants identified in the STIRPAT model) are independent with each other.

Journal Pre-proof Such an assumption, however, is unrealistic as revealed by some latest studies. For example, Liu et al. (2017) suggest that energy consumption in adjacent Chinese regions are correlated. Yu (2012) and Li et al. (2018) both point out that regional energy intensity in China has spatial linkage, i.e., energy efficiency in one region affects energy efficiency in neighboring regions. Along this line, we expect that not only CO2 emissions but also energy price and other energy market factors in adjacent regions may possess spatial correlation. As a result, non-spatial methods applied by

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prior studies that ignore autocorrelation between observations may suffer from serious

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estimation bias and inconsistency (Elhorst, 2010). We instead conduct our analysis by

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utilizing spatial econometric models to simultaneously control for the influence of

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spatial dependency between observations and across time (Anselin, 1988; Pinkse and Slade, 2010). Applying this method to the sustainability research is a main

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methodological contribution of our study, and it opens the door for future

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sustainability and environmental studies to explicitly consider spatial effects between

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observations.

The rest of the paper is organized as follows. Section 2 introduces our empirical methods including the extended STRIPTAT model, sample and variable measurements, and spatial panel data models applied in this study. Section 3 presents our empirical results and we conclude the paper with a discussion in Section 4.

2. Research Methods 2.1 STIRPAT Model In order to explore the impact of energy price on carbon emissions, we construct an extended STIRPAT model based on the IPAT equation proposed by Ehrlich and

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Holdren (1972), which is a widely adopted modeling framework to assess environmental impacts of human activities. The IPAT model estimates the environmental impact on human activity (I) as the product of population size (P), per capita wealth (A) and technology (T). Dietz and Rosa (1994) transform the original IPAT equation into a random form and proposed the STIRPAT model, so called

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Stochastic Impacts by Regression on Population, Affluence and Technology model,

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which is mathematically defined as follows:

(1)

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I  aPb AcT d e

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Where I, P, A, T are defined as in the IPAT model elaborated above, a, b, c, d are

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coefficients to be evaluated, and e represents the random disturbance term. Taking the

model:

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logarithm of both sides of equation (1), we obtain the following linear regression

(2)

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𝑙𝑛𝐼 = 𝑎 + 𝑏𝑙𝑛𝑃 + 𝑐𝑙𝑛𝐴 + 𝑑𝑙𝑛𝑇 + 𝑙𝑛𝑒

The parameters b, c, and d in equation (2) represent the elasticity of environment impacts of population, wealth, and technology respectively. Because many technological factors have environmental impact, it is rather difficult to find exact proxy variables for the technology term in Equation 2 in practice. York et al. (2004) suggest that it is legitimate to add variables affecting technology to model (2) to construct an extended STIRPAT model as long as these variables conform to the concept of technological multiplier and do not violate the product principle of IPAT equation. In another words, T must be able to be expressed

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as the product of its influencing factors. We thus compile an extended STIRPAT model with Population proxies including population size (POP) and urbanization ratio (URB), Affluent measure of GDP per capita (PGDP), Technology proxies including industry structure (INS), foreign direct investment (RFDI), education level (EDU), energy utilization efficiency (TEC), energy consumption structure (ENS), and energy

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price (EP) being our main variable of interest. Our basic empirical model is expressed

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as follows:

ln CO2    1 ln POP   2 ln PGDP  3 ln INS   4 ln URB  5 ln RFDI

(3)

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 6 ln TEC  7 ln EDU  8 ln ENS  9 ln EP  

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2.2 Data Sources

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This paper uses data from 2001 to 2016 in 30 Chinese provinces including central

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government controlled municipalities. Data of energy consumption and energy price are obtained from provincial statistical yearbooks, China Environmental Yearbook,

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China Energy Statistics Yearbook and China Compendium of Statistics. Population related variables are obtained from China Statistical Yearbook and China Demographic Yearbook. Data on regional GDP, industrial structure and foreign direct investment are obtained from provincial statistical yearbooks and China Compendium of Statistics. Our total sample has 480 observations. 2.3 Variable Measurements and Descriptive Analysis of Variables Our dependent variable is Carbon dioxide emissions denoted as CO2 (unit: ten kiloton). Because fuel usage is the major course of CO2 emissions, we follow the method proposed by Tian et al. (2015) to aggregate carbon emissions of major energy

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products, including coal, fuel oil, crude oil, natural gas, gasoline, diesel, kerosene, and liquid petroleum as well as cement production for each region at each sample year. In the calculation, consumption of every energy product is converted into a unified standard coal unit, and then times CO2 emission coefficient of each energy product to obtain CO2 emissions.5

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Our main independent variable is Energy Price, denoted as EP (unit: RMB per kg

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standard coal). We follow the method proposed by Lin and Li (2014) to convert

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consumption of different energy products into a unified standard coal unit, and then use consumption ratio of each energy product as a weight to calculate the

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respective prices.

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comprehensive energy price index as a weighted average of these energy products’

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Our model also includes other control variables that may influence CO2 emissions as specified in Equation 3. First, POP (unit: ten thousand) measures total

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population of the province at the end of the sample year. Increasing population and human activities have long been found to be a key contributor of greenhouse emissions, we thus expect that population growth will increase CO2 emissions (Diez & Rosa, 1994). Second, we include URB (unit: %) the urbanization index as another population measure. This index is calculated as the number of people residing in urban areas divided by the total population of a province. A higher urban population is typically associated with more greenhouse emissions due to difference in

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The specific calculation formula is CO2 

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  E i 1

i

i

i

Where  i ,  i , Ei are standard coal transform

rates, CO2 emission coefficient and consumption of the i-th energy product identified above.

Journal Pre-proof consumption patterns of urban and rural population (Liu et al., 2017). Third, PGDP (unit: RMB per person) captures GDP per capita, which is calculated by dividing provincial GDP by total population to capture affluence in the SPRIPAT model. We expect that more affluent regions will be associated with more energy consumption, which will result in larger CO2 emissions (Narayan & Narayan, 2010). In addition, we include a series of technology related factors that will influence CO2 emissions as identified in prior studies. INS (unit: %) measures industry

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structure calculated using the ratio of provincial industry value-added to GDP ratio

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(Liu et al., 2017). It comprises value added in mining, manufacturing, construction,

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electricity, water, and gas. Value added is the net output of a sector after adding up all

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outputs and subtracting intermediate inputs. This measure is used to capture regional energy consumption structure. Given these aforementioned industrial sectors consume

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more energy than agricultural and service sectors, a region dominated by these

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industries will be associated with higher energy consumption and larger CO2 emissions. In addition, RFDI (unit: %): is a measure of foreign direct investment

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intensity calculated by dividing foreign direct investment amounts in each province to the provincial GDP value. On one hand, the technology spillover effect brought by foreign companies could help local firms improve production technology and reduce energy consumption. On the other hand, the introduction of foreign businesses can enhance market competition, and prompt local enterprises to improve their energy consumption efficiency (Zhou et al., 2018). Taken together, we expect a negative influence of RFDI on CO2 emissions. Moreover, EDU (unit: year) captures residents’ education level, which is measured by the weighted average of years of schooling of

Journal Pre-proof residents in the region following methods of Li et al. (2018).6 Well-educated citizens are generally more acceptable to the idea of energy conservation and environmental protection and are thus more likely to implement this concept in their daily lives (Liu et al., 2017). These citizens are also more environmental conscious, hence will urge their local governments to pay more attention to environmental issues such as energy production efficiencies and consumption structures of industries in the region (Li, et al., 2018). Taken together, we expect that a region with more better-educated

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residents will be able to use energy products more efficiently, thus being associated

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with lower CO2 emissions.

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Our control variables also include other factors capturing energy market

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characteristics. TEC (unit: %) indicates energy utilization rate, represented by the ratio of GDP to energy consumption, the higher the ratio, the higher the energy

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utilization efficiency. We expect that higher energy utilization efficiency is negatively

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associated with CO2 emissions (Li et al., 2012). In addition, ENS (unit: %) indicates energy consumption structure, which is measured by the ratio of coal consumption to Because different energy sources cannot be directly

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total energy consumption.

added together, we convert all kinds of energy consumption into standard coal in the calculation of total energy consumption and use it as the denominator of the ratio. Coal is not only more inefficient but also more polluted compared to other energy products (Lin and Zhu, 2017). Therefore, the higher the ratio of coal consumption to total energy consumption in a region, the higher the CO2 emissions. To improve data

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This variable

is calculated

as:

EDU 

6  POP1  9  POP2  12  POP3  16  POP4 Where POP

POPi , i  1, 2,3, 4 indicate the number of people with academic qualifications for elementary school, junior high school, high school, and university respectively. In China, the years of schooling required to obtaining elementary school, junior high school, high school, and university degrees are 6, 9, 12,and 16 years respectively, which corresponds to the coefficients in the above equation.

Journal Pre-proof interpretability, we take logarithms of all dependent and independent variables. Descriptive statistics of these aforementioned variables are present in Table 1. We present mean, median, standard deviation, minimum and maximum values of each major variable used in the analysis along with a brief variable description. *** Insert Table 1 about Here*** 2.4 Spatial Panel Data Models

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Our main analysis was conducted using spatial panel data models. The spatial

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econometric analysis is originally developed by Anselin (1988). Our model is built on

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Elhorst (2010)’s spatial panel data model that combines advantages of both a spatial model and panel data model. The essence of our design is to consider spatial effects of

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CO2 emissions, i.e., an area’s contributing factors to CO2 emissions may affect CO2

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emissions of its neighboring areas and vice versa.

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Generally speaking, there are three kinds of spatial panel data models: spatial lag regression model (SLR), spatial error panel model (SEM) and spatial autocorrelation

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panel model (SAR). The general form of these spatial panel data models is expressed as below following terminologies used by Lesage & Pace (2009): 𝑙𝑛𝐶𝑂2𝑖𝑡 = 𝛼𝑖 + 𝛾𝑙𝑛𝐸𝑃𝑖𝑡 + 𝛽𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑖𝑡 + 𝜌𝑊 × 𝑙𝑛𝐶𝑂2𝑖𝑡 + µ𝑖𝑡 + 𝜂𝑡 + 𝜉𝑡

(4)

µ𝑖𝑡 = 𝜆𝑊𝜐𝑖𝑡 + 𝜀𝑡

Here i denotes province and t represents time. lnCO2 is the CO2 emission vector of each province in period t. EP is the vector of our main independent variable, energy price. Control represents the matrix of control variables described above. W is the spatial weight matrix that captures spatial correlations in our data. W is incorporated in Equation 5 in two ways. First, W interacts with the spatially lagged dependent

Journal Pre-proof variable lnCO2 as one of the regressors where ρ is the spatial correlation coefficient. Second, W is included in the spatially dependent stochastic disturbance term µt, It captures the disturbance of each cross-sectional unit in data υ in period t. The spatial correlation coefficient of this term is captured by λ and εt is random disturbance. In addition, 𝛼𝑖 is provincial fixed effect, ηt denotes time effect, and ξt is random

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disturbance that is independent and identically distributed. The spatial autocorrelation model described above could also be simplified in two

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ways. First, when 𝜌≠0 and λ=0, Equation 4 can be simplified as a Spatial Lag

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Regression Model (SLR) that only captures spatial correlation using the interaction of

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spatial weight matrix W and lnCO2, with ρ being the spatial correlation coefficient.

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The form is expressed as follows. All variables are the same as described above. 𝑙𝑛𝐶𝑂2𝑖𝑡 = 𝛼𝑖 + 𝛾𝑙𝑛𝐸𝑃𝑖𝑡 + 𝛽𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑖𝑡 + 𝜌𝑊 × 𝑙𝑛𝐶𝑂2𝑖𝑡 + 𝜂𝑡 + 𝜉𝑡 + 𝜀𝑡

(5)

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In contrast, when ρ=0 and λ≠0, Equation 4 can be simplified as a Spatial Error

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Panel Model (SEM) that only captures spatial correlation using the interaction of the spatial matrix W and the cross-sectional disturbance term υ. 𝑙𝑛𝐶𝑂2𝑖𝑡 = 𝛼𝑖 + 𝛾𝑙𝑛𝐸𝑃𝑖𝑡 + 𝛽𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑖𝑡 + 𝜆𝑊𝜐𝑖𝑡 + 𝜂𝑡 + 𝜉𝑡 + 𝜀𝑡

(6)

Where  represents spatial correlation coefficient and vit is the spatial error term. All other variables are the same as described above. Finally, when 𝜌≠0 and λ≠0, both the spatial lag effect and the spatial error effect remain as the complete Spatial Autocorrelation Panel Model (SAC) as described in its general form in Equation 4. There are many different ways to compute the spatial weight matrix (W). The most commonly used methods are binary adjacency matrix, K-nearest Neighbors matrix and distance threshold matrix. We adopt the binary adjacency matrix in our

Journal Pre-proof main analysis. That is, if the two regions have a common boundary, the weight of each other is set to 1, and 0 otherwise. In order to improve statistical properties of model estimation, we take a row normalization of the spatial weight matrix. We also use other alternative weighting methods in our sensitivity analysis.

3. Empirical Results

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3.1 Test of Spatial Effect and Model Selection

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Before estimating any spatial econometric models, it is essential to test the existence

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of spatial effects in our sample. Following methods recommended by Elhorst (2010),

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we applied three different tests to validate the existence of spatial effect, namely

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Moran's I test, Geary's C test and Getis-Ords G test and present our test statistics in Table 2. All three tests present in Table 2 reject the null hypotheses of no spatial

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effects, thus confirms the existence of spatial correlation in Chinese regions.

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*** Insert Table 2 about Here***

We next applied model specification tests suggested by Elhorst (2014) to select an appropriate spatial model for our analysis. According to Elhorst (2014), the first step in model specification is to estimate a non-spatial model and use the Lagrange Multiplier (LM) test to determine whether to establish a spatial lag regression model specified in Equation 5 or a spatial error model specified in Equation 6. We estimated four different non-spatial panel data models and contrast each model’s estimation results with both spatial lag models and spatial error models. Two types of Lagrange Multiplier (LM) tests were performed, the classical LM test proposed by Anselin et al. (2006) and the robust LM test proposed by Elhorst (2010). Both sets of LM test results are present in Table 3, with column 1 on the pooled OLS

Journal Pre-proof model, column 2 on the cross-sectional fixed effect model, column 3 on the time-series fixed effect model, and column 4 on the double fixed-effect model including both cross-sectional and time-series fixed effects. *** Insert Table 3 about Here*** Results from the classical LM test and the robust LM test consistently support the existence of the spatial lag effect, while not as consistent for the spatial error effect.

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For the former, seven out of eight model specification tests are statistically significant at 0.05 level, while only two out of eight tests are statistically significant for spatial

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error models. Overall, our results confirm the necessity to consider spatial interactions

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in our empirical models. In addition, the spatial lag model better reflects our data

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structure than the spatial error model, and cross-sectional fixed effect models are most

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appropriate.7 We thereby apply the spatial lag cross-sectional fixed effects panel data

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model as the main model to analyze the impact of energy price on CO2 emissions.

3.2 Main Estimation Results

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Our main spatial econometric analysis results are present in Table 4. We estimated the spatial lag cross-sectional fixed effects panel data model (SLR) in column 1. For comparison and robustness analysis, we also present estimation results of spatial error model (SEM) in column 2, and results of spatial autocorrelation model incorporating both spatial lag and spatial error effects in (SAC) in column 3. W*lnCO2 and W*ERRO represent the spatial lag term and the spatial error term respectively, and their coefficients are the estimation of spatial correlation coefficients  and 

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Panel data models include both fixed effects and random effects models. Our Hauseman test results

(table omitted) indicate that the fixed effects model is a better choice than the random effect model (χ2=71.05, p=0.00).

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explained above. *** Insert Table 4 about Here*** Table 4 indicates that spatial correlation coefficients of models 1, 2 and 3 (W*lnCO2 and/or W*ERRO) are all significant at 1% level, which again confirm the existence of spatial correlation in the impact of energy price on CO2 emissions in

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China. More specifically, Column 1 of Table 5 shows that the coefficient of the spatial lag term (W*lnCO2) is positive and significant with an estimated value of 0.30,

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indicating that an average 1% reduction in CO2 emissions in neighboring regions will

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lead to a 0.30% CO2 emission reduction in the focal area. This result indicates strong

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spatial spillover effects on CO2 emissions. Importantly, the coefficient of energy

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price (lnEP) is negative and significant with an estimated value of 0.165. That is to

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say, every 1% increase in energy price will result in a 0.165% decrease in CO2 emissions. This result suggests that energy price can play an important role in

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reducing regional CO2 emissions.

The estimation results of other coefficients in Column 1 of Table 4 are also valuable and worth mentioning. First, we notice that population growth boosts CO2 emissions, where a 1% population growth is associated with a 0.508% increase in CO2 emissions. Moreover, urbanization ratio has a positive and significant impact on CO2 emissions. Every 1 % increase in the urbanization ratio is associated with 0.402% increase in regional CO2 emissions. In addition, economic growth is found to be a main contributor of CO2 growth, a 1% increase in per capita GDP is associated with a 0.657% increase in CO2 emissions. This result suggests that China's economic growth

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is accomplished by increasing energy consumption and CO2 emissions. If per capita GDP growth maintains at its current 6.1% level, the environmental cost of China's economic growth will be more than 4% of CO2 emissions growth per year. Table 4 also suggests that industrial structure has a positive impact on CO2 emissions. A 1% increase in the proportion of manufacturing industry will result in a

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0.214% growth of carbon emissions. We also find that foreign direct investment helps reduce CO2 emissions. A 1% increase in the ratio of FDI to GDP decreases CO2

ro

emissions by 0.056%. In addition, increasing education level negatively affects CO2

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emissions. With every 1% increase in residents’ years of schooling, CO2 emissions is

re

reduced by 1.627%. Moreover, the improvement of energy utilization efficiency helps

lP

reduce CO2 emissions. A 1% rise in energy utilization efficiency results in a 0.302%

na

reduction in CO2 emissions. Finally, we find that the impact of energy consumption structure on CO2 emissions is positive and significant. A 1% increase in the

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proportion of coal consumption in will lead to a 0.092% growth of CO2 emissions. 3.3 Decomposition of Marginal Effects We next report marginal effects of our independent variables on CO2 emissions following methods proposed by LeSage and Pace (2009). According to them, purely relying on point estimation results of spatial econometric models to draw a conclusion about the existence of spatial correlation effect is problematic. Because all independent variables may have both a direct and an indirect effect on the dependent variable, reporting an aggregated composite effect is unable to fully reflect the underlying relationships between variables. We hereby decomposed the impact of

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each contributing factor on CO2 emissions estimated by spatial lag cross-section fixed effects panel data model into direct and indirect effects. The direct effect represents the marginal impact of an influencer on the focal region’s CO2 emissions, while indirect effect refers to the marginal impact of other regions’ influencers on CO2 emissions of the focal region. Therefore, indirect effect can be seen as a spatial

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spillover effect. The decomposition results of these marginal effects are given in Table 5.

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*** Insert Table 5 about Here***

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As shown in Column 1 of Table 5, the direct elasticity of energy price to CO2

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emissions are -0.169. This result suggests that a 1% increase in energy price will

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directly result in a 0.169% decline in focal region’s CO2 emissions. In addition,

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because China's energy market is centrally controlled made up of different regional markets, energy price among various regions are highly correlated. As a result,

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changes in energy price of one region will be transmitted to neighboring regions’ energy market. Table 5 indicates that an indirect effect of -0.070, i.e., a 1% increase in energy price of adjacent regions will result in a 0.070% reduction in CO2 emissions of the focal region. Taken together, a 1% rise in energy price will lead to an overall 0.239% reduction in CO2 emissions of the focal region. These results indicate that energy price could be an effective regulatory tool to reduce regional CO2 emissions. Table 5 also reveals marginal effects of other factors in our model. For example, we notice that population size and urbanization ratio both have positive influences on CO2 emissions directly and indirectly. That is to say, population growth and

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increasing urbanization in the focal region and adjacent regions will both increase CO2 emissions of the focal region. GDP per capita also has both direct and indirect positive influences on CO2 emissions. These results indicate that increasing affluence in the focal region and adjacent regions will both increase CO2 emissions of the focal region. A similar positive impact is observed in industry structure in term of both

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direct and indirect channels. Moreover, increasing foreign direct investment and education levels both reduce CO2 emissions, through both a direct influence of the

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region and indirect effects of adjacent regions. In addition, increasing energy

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efficiency is associated with reduced CO2 emissions, with significant direct and

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indirect effects documented in Table 5. That is to say, improving energy efficiency

lP

will not only reduce CO2 emissions of the focal region, but also have spillover effects

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to adjacent regions. Finally, Table 5 reveals that CO2 emissions in the focal region will increase with coal consumption in both the focal region’s energy structure and

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those of adjacent regions.

3.4 Sensitivity and Robustness Analyses China’ economic development is often driven by growth in industries with high pollution, high energy consumption, and high carbon emissions at the cost of environment (Wang et al., 2016). The adverse effect of this economic development mode is unable to be mitigated in the short-term (Narayan and Narayan, 2010). Therefore, CO2 emissions may be path dependent, with CO2 emissions in the last period affecting CO2 emissions in the recent period. In order to control the effect of path dependence in CO2 emission, we hereby estimated a dynamic spatial lag

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cross-sectional fixed effects panel data model (DSAR) by adding a lag value of the dependent variable to the SAR model estimated in Column 1 of Table 3 and specified in Formula (5). This model is specified as follows: 𝑙𝑛𝐶𝑂2𝑖𝑡 = 𝛼𝑖 + 𝛾𝑙𝑛𝐸𝑃𝑖𝑡 + 𝛽𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑖𝑡 + 𝜌𝑊 × 𝑙𝑛𝐶𝑂2𝑖𝑡 + 𝛿𝑙𝑛𝐶𝑂2𝑖𝑡−1 + 𝜂𝑡 + 𝜉𝑡 + 𝜀𝑡 (7)

Where ln CO 2it 1 represents the lag value of logarithm of CO2 emissions, which is

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included as an additional regressor. ρ is the spatial correlation coefficient of the spatial lag effect of W×lnCO2. Other variables are the same as in Formula 5. We

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report estimation results of the DSAR model in Column 1 of Table 6.

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***Insert Table 6 about Here***

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Table 6 shows that the coefficient of lnCO2it-1 (denoted as Lag.lnCO2) is

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positive and significant with a value 1.295, which suggests that a 1% increase in last

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period CO2 emissions will result in a 1.295% increase in current period CO2 emissions. This result confirms the existence of path dependence in China’s CO2

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emissions. We also notice that the coefficient of energy price (lnEP) is -0.186 and significant at 0.01 level, which indicates that a 1% increase in energy price will reduce CO2 emission levels by 1.86%. The spatial correlation coefficient is 0.279, also significant at 0.01 level. Coefficients of other variables are also qualitatively similar to those reported in Table 4. These results indicate that our estimates are robust to the incorporation of path dependence of CO2 emissions. Change in energy price may not immediately affect energy consumption since businesses and consumers are usually unable to adjust production plans and consumption patterns in a short period of time. Therefore, the impact of energy price

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on CO2 emissions may have a lag effect. We thereby replaced the energy price variable (EP) with a lagged EP variables (Lag. lnEP) and re-estimate all three models specified in Table 4, namely the SLR, SEM and the SAC models. The estimation results are list in Columns 2, 3 and 4 of Table 6 respectively. We find that lag energy price has a consistently negative impact on CO2 emissions in all three models, with a

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1% increase in lag energy price being associated with 1.43% to 1.80% decrease in CO2 emissions. These results again confirm our main prediction. Coefficients of other

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variables are also close to our main results reported in Table 4 in terms of both

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numerical values and statistical significance. Our results are thus robust to the

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incorporation of lagged independent variable.

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In our main results, we used the binary adjacency matrix to construct the spatial

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weight matrix. This matrix assumes that if two regions have a common boundary, the weight of each other is set to 1, and 0 otherwise. To control for the impact of spatial

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weight matrix on our estimation result, we re-estimated our SLM model specified in Equation 4 by applying the K-nearest neighbor method to construct spatial weight matrices and setting the value of K as 3, 4, 5, 6 respectively. This method assigns the k nearest neighbors a weight 1/k and all others areas 0. Estimation results using these different spatial weight matrices are reported in Table 7. Results from Table 7 are qualitatively the same as those reported in our main analysis. In particular, we notice that increasing energy price significantly reduces provincial CO2 emissions in China, with the coefficients ranging from -0.127 to -0.168. These results thus again confirm our main hypothesis.

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***Insert Table 7 about Here***

4. Discussion and Conclusion Using panel data of 30 provinces in China from 2001 to 2016, this paper applied spatial econometric techniques to investigate the impact of energy price and other factors on China’s CO2 emissions. We incorporated energy market factors into the

of

traditional STIRPAT human ecology model to conduct our analysis. We identify a

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significant negative impact of energy price on CO2 emissions. Importantly, we

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document both a direct effect of the focal region’s energy price on CO2 emissions and

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an indirect spatial spillover effect exerted by adjacent regions. The direct and indirect

lP

effects are also observed in other contributors of CO2 emissions identified in the extended STIRPAT model, including population size, urbanization ratio, GDP per

na

capita, industry structure, foreign direct investment, education level, energy

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consumption structure and energy efficiency. Our study also documents a path dependent pattern in China’s provincial CO2 emissions and validates that the negative impact of energy price on CO2 emissions remains qualitatively the same after incorporating the influence of previous period CO2 emissions. In addition, our empirical results are robust to lagged energy price measures, alternative spatial economic models, and alternative spatial weight matrices. Our results indicate that energy price plays an important role in affecting energy consumption patterns and ultimately influencing greenhouse emissions caused by energy consumption, thus CO2 emissions can be effectively suppressed by raising

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energy price. This result is consistent with the prediction of the neoclassical economic theory (Ferguson, 1969). If a commodity’s price rises relative to the price of its substitutes, the demand for this commodity will drop. In our case, energy could be viewed as a resource commodity even in an inefficient market such as China. As a result, when energy price increases, producers may substitute energy consumption

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with other production factors such as labor or technology by employing more workers and utilizing energy saving technologies, etc. All these efforts will result in a

ro

reduction in CO2 emissions caused by energy consumptions. The elasticity of energy

-p

price on CO2 emissions however is still relatively small, which may be caused by the

re

inefficiency of the Chinese energy market subject to heavy government regulations.

lP

As a result, reducing the tight governmental control and accelerating the

na

marketization reform of the energy market may facilitate the energy industry to play a larger role in shaping energy consumption patterns and curbing greenhouse emissions.

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Moreover, our results indicate that a region’s CO2 emissions are not only affected by the energy price of the focal region, but also influenced by energy price of adjacent regions thus are subject to spatial spillover effects of neighboring regions’ energy markets. More specifically, the direct elasticity of energy price to CO2 emissions is -0.169 and the indirect elasticity is -0.070, with the direct effect being about twice the magnitude of the indirect effect. These results suggest that CO2 consumptions are mainly influenced by the adjustment of energy price in the focal region, while the impact of other regions’ energy price on energy consumption levels of the focal region is limited. This result may be caused by high regional barriers in

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may be essential in encouraging the positive spillover effects of energy market factors between regions.

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Our result also indicates that China’s CO2 emissions have a path dependent

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characteristics, with CO2 emissions of the past positively influencing emission levels

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of the current period. This pattern may be attributed to a lack of strict environmental

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regulation enforcement in China that results in an unsustainable economic growth

na

route neglecting the adverse environmental impact. In recent years, the Chinese central government has added environment appraisals and audit in local governments’

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performance evaluation system, which is a significant advancement compared to the old evaluation system mainly driven by local economic growth regardless of its environmental impact (Pan, et al., 2019). Following this more comprehensive evaluation system to explicitly embrace environmental impacts as a key decision factor in designing regional economic growth plans may be beneficial for a nation to achieve sustainable economic growth. In summary, this paper utilizes the spatial panel data model to examine the impact of provincial energy price on CO2 emissions in China. This research topic is urgent given China being the largest CO2 emitter in the world while with an

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ambitious goal to considerably reduce its CO2 emissions in the next decade. Our paper offers additional insights on how an energy market could play a critical role in shaping energy consumption patterns, and ultimately reducing greenhouse emissions to achieve sustainable economic growth. Although our paper is positioned in China, it also has implications for other emerging nations who likewise face the dilemma of

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achieving economic growth and mitigating its environmental costs. We hope that our study could set the stage for a richer understanding of the nature of human actions on

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greenhouse emissions in the broad environmental and sustainability research and form

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the basis for comprehensive policy formulation to facilitate a sustainable growth of

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na

lP

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China and world economy.

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Narayan PK, Narayan S. 2010. Carbon dioxide emissions and economic growth: Panel data evidence from developing countries. Energy Policy, 38(1): 661-666. Pan, W., Pan, W., Hu, C., Tu, H., Zhao, C., Yu, D., Xiong, J., Zheng, G. 2019. Assessing the green economy in China: An improved framework. Journal of Cleaner Production, 209(1): 680-691. Pang J, Wen X, Sun X. 2016. Mixing ratio and carbon isotopic composition investigation of atmospheric CO2 in Beijing, China. Science of the Total Environment 539:322-330. Pinkse, J. Slade, M. 2010. The future of spatial econometrics. Journal of Regional Science, 50(1): 103-117. Shuai C, Chen X, Wu Y, 2018. Identifying the key impact factors of carbon emission in China: Results from a largely expanded pool of potential impact factors. Journal of Cleaner Production, 175: 612-623. Tao, X.M., Xing, J.W., & Huang, X. 2009. The Measurement of energy price

Journal Pre-proof distortions and factor substitution in Chinese industry. The Journal of Quantitative & Technical Economics, (11), 3-16. Vanneste J, Van G T, Vander P E, 2011. Energetic valorization of wood waste: estimation of the reduction in CO2 emissions. Science of the Total Environment, 409(19):3595-3602. Wang S, Fang C, Guan X. 2014. Urbanization, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China’s provinces. Applied Energy, 136: 738-749.

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Journal Pre-proof Zhang Y, Zhang S. 2018. The impacts of GDP, trade structure, exchange rate and FDI inflows on China's carbon emissions. Energy Policy, 120: 347-353. Zhou X, Zhang J, Li J. 2013. Industrial structural transformation and carbon dioxide emissions in China. Energy Policy, 57(3): 43-51. Zhou Y, Fu J, Kong Y. 2018. How Foreign Direct Investment Influences Carbon Emissions, Based on the Empirical Analysis of Chinese Urban Data. Sustainability, 10(7): 1-19.

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Table 1. Descriptive Statistics of Main Variables Mean

Median SD

Min

Max

lnCO2

CO2 emissions

10.010 10.036 0.820 6.919 11.795

lnEP

Energy price

0.360

0.393

0.367 -0.879 1.227

lnPOP

Population

8.152

8.247

0.758 6.260 9.292

lnURB

Urbanization rate

3.857

3.844

0.290 3.198 4.495

lnPGDP Affluent

9.983

10.041 0.777 8.006 11.589

lnINS

Industrial structure

3.649

3.717

0.241 2.574 3.971

lnRFDI

Foreign direct investment

0.600

0.725

0.998 -2.684 2.684

lnEDU

Education level

2.063

2.063

0.141 0.782 2.455

lnTEC

Energy utilization rate

-0.090 -0.076

lnENS

Energy consumption structure 4.154

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Variable Description

0.316 2.617 6.965

-p

4.211

0.718 -1.564 10.183

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Table 2. Spatial Correlation Tests Geary’s C

P value

0.115

0.001

statistic

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na

statistic

lP

Moran’s I

0.787

Getis-Ords G

P value

statistic

P value

0.000

-0.115

0.001

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Table 3. LM Tests of Spatial Model Specifications Test method LM  (spatial

Pooled model 13.328 (p=0.000)

Cross-sectional fixed effects model 9.646 (p=0.002)

Time fixed effects model 2.392 (p=0.122)

Double fixed effects model 4.465 (p=0.035)

1.743 (p=0.187)

5.318 (p=0.021)

0.993 (p=0.319)

2.086 (p=0.149)

13.047 (p=0.000)

4.541 (p=0.033)

6.252 (p=0.012)

3.032 (p=0.082)

1.462 (p=0.227)

0.212 (p=0.645)

lag model) LM  (spatial

error model) Robust LM 

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na

lP

re

-p

(spatial error model)

4.852 (p=0.028)

ro

Robust LM 

of

(spatial lag model)

0.653 (p=0.419)

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lnINS lnRFDI lnEDU lnTEC lnENS W*lnCO2

2

(0.001) 0.747

(0.001) 0.812

(0.001) 0.783

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W*ERRO

R-squared

of

lnPGDP

ro

lnURB

(3) SAC -0.161*** (0.061) 0.556*** (0.157) 0.356*** (0.124) 0.772*** (0.069) 0.184*** (0.067) -0.051*** (0.013) -1.689*** (0.190) -0.294*** (0.031) 0.070 (0.053) 0.127 (0.091) 0.403*** (0.139) 0.013***

-p

lnPOP

(2) SEM -0.161** (0.066) 0.650*** (0.143) 0.352*** (0.121) 0.837*** (0.049) 0.152** (0.068) -0.049*** (0.012) -1.667*** (0.192) -0.284*** (0.031) 0.064 (0.053) --0.531*** (0.084) 0.012***

re

lnEP

(1) SLR -0.165*** (0.056) 0.508*** (0.152) 0.402*** (0.131) 0.657*** (0.058) 0.214*** (0.062) -0.056*** (0.013) -1.627*** (0.184) -0.302*** (0.030) 0.092* (0.051) 0.300*** (0.060) --0.012***

lP

Variables

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Table 4. Main Results from Spatial Panel Data Models

Note: Standard errors are reported in parentheses. Statistical significance are designated with asterisks with *** indicating p<0.01, ** indicating p<0.05, and * indicating p<0.10.

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Table 5. Decomposition of Marginal Impact

lnRFDI lnEDU lnTEC lnENS

of

lnINS

ro

lnPGDP

Indirect effect -0.070** (0.035) 0.206*** (0.071) 0.172** (0.069) 0.266*** (0.060) 0.091** (0.036) -0.024*** (0.009) -0.668*** (0.186) -0.123*** (0.032) 0.034* (0.017)

-p

lnURB

lP

lnPOP

Direct effect -0.169*** (0.053) 0.522*** (0.156) 0.426*** (0.137) 0.667*** (0.048) 0.226*** (0.070) -0.060*** (0.015) -1.658*** (0.188) -0.307*** (0.030) 0.088** (0.043)

re

Variables lnEP

Total effect -0.239*** (0.084) 0.727*** (0.211) 0.598*** (0.195) 0.933*** (0.070) 0.316*** (0.099) -0.085*** (0.023) -2.326*** (0.316) -0.430*** (0.050) 0.122** (0.059)

Note: Standard errors are reported in parentheses. Statistical significance are designated with asterisks

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with *** indicating p<0.01, ** indicating p<0.05, and * indicating p<0.10.

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Table 6. Sensitivity and Robust Analyses

lnINS lnRFDI lnEDU lnTEC lnENS W*lnCO2 W*ERRO

of

lnPGDP

ro

lnURB

(3) LSEM -----0.143** (0.060) 0.678*** (0.148) 0.313** (0.133) 0.839*** (0.050) 0.128** (0.063) -0.052*** (0.012) -1.666*** (0.188) -0.308*** (0.031) 0.143*** (0.052) --0.378*** (0.095)

-p

lnPOP

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Lag. lnEP

lP

lnEP

(2) LSLR -----0.180*** (0.056) 0.618*** (0.153) 0.374*** (0.139) 0.729*** (0.059) 0.143** (0.058) -0.053*** (0.013) -1.647*** (0.180) -0.319*** (0.029) 0.155*** (0.050) 0.210*** (0.059) ---

na

Lag.lnCO2

(1) DSAR 1.295*** (0.117) -0.186*** 0.058 --0.738*** (0.067) 0.292** (0.139) 0.502*** (0.071) 0.479*** (0.095) -0.011 (0.014) -0.304** (0.135) -0.097*** (0.030) 0.100 (0.074) 0.279*** (0.067) ---

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Variables

(4) LSAC -----0.161*** (0.060) 0.623*** (0.155) 0.333** (0.137) 0.790*** (0.067) 0.138** (0.061) -0.053*** (0.013) -1.676*** (0.185) -0.314*** (0.030) 0.145*** (0.051) 0.104 (0.088) 0.261* (0.147)

Note: Standard errors are reported in parentheses. Statistical significance are designated with asterisks with *** indicating p<0.01, ** indicating p<0.05, and * indicating p<0.10.

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Table 7. Estimation Results Using Different Spatial Weight Matrices

lnRFDI lnEDU lnTEC lnENS W*lnCO2 R-squared

of

lnINS

(3) K=5 -0.140** (0.056) 0.511*** (0.158) 0.288** (0.134) 0.735*** (0.053) 0.198*** (0.063) -0.049*** (0.013) -1.693*** (0.188) -0.325*** (0.030) 0.127** (0.052) 0.234*** (0.056) 0.794

ro

lnPGDP

(2) K=4 -0.133** (0.057) 0.557*** (0.158) 0.304** (0.135) 0.754*** (0.052) 0.214*** (0.063) -0.048*** (0.013) -1.712*** (0.189) -0.329*** (0.031) 0.131** (0.052) 0.193*** (0.054) 0.811

-p

lnURB

lP

lnPOP

na

lnEP

(1) K=3 -0.127** (0.056) 0.594*** (0.154) 0.313** (0.134) 0.740*** (0.053) 0.224*** (0.063) -0.044*** (0.013) -1.676*** (0.189) -0.322*** (0.031) 0.126** (0.052) 0.196*** (0.052) 0.816

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VARIABLES

(4) K=6 -0.168*** (0.056) 0.448*** (0.156) 0.247* (0.133) 0.688*** (0.054) 0.182*** (0.063) -0.048*** (0.013) -1.649*** (0.185) -0.317*** (0.030) 0.127** (0.051) 0.335*** (0.061) 0.772

Note: Standard errors are reported in parentheses. Statistical significance are designated with asterisks

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with *** indicating p<0.01, ** indicating p<0.05, and * indicating p<0.10.

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Graphical abstract

Highlights

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Examine the impact of energy price on regional CO2 emissions in China. Energy price has a significant negative impact on China's CO2 emissions directly and indirectly. Energy price and other economic factors possess spatial spillover effects. Energy market factors are essential in curbing CO2 emissions. Applying spatial econometric techniques in sustainability and energy research.

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