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Emission reduction target, complexity and industrial performance
Journal of Environmental Management 260 (2020) 110148
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Journal of Environmental Management journal homepage: http://www.elsevier.com/locate/jenvman
Research article
Emission reduction target, complexity and industrial performance Zhaoyingzi Dong a, Wenqiang Chen b, Shaojian Wang c, * a
School of Public Affairs, Zhejiang University, Zhejiang, China School of Accounting, Zhejiang University of Finance & Economics, Zhejiang, China c Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China b
A R T I C L E I N F O
A B S T R A C T
Keywords: Climate change “Emission reduction target” policy Complexity Industrial diversification 11th five-year plan China
To tackle climate change, Chinese government has applied an array of mitigating measures to reduce CO2 emissions. During the 11th Five-year Plan, Chinese government set the emission reduction target policy to reduce energy consumption per unit of gross domestic product (GDP) by 20%. This paper attempts to estimate the effect of this emission reduction target policy on industrial performance from complexity perspective. The result shows more complex industrial structure is related to less coal consumption. In general, emission reduction target policy has a negative effect on the probability of branching into new industries and lower the productivity and prof itability of a given industry. However, this negative effect is weaker for more complex industries. Only for in dustries with very high complexity, emission reduction target policy can improve the performance of an industry rather than exerting a negative effect. Our finding not only helps to design a more effective policy to achieve industrial development strategy, but also provides a potential way to achieve economic growth while reduce the emission of greenhouse gases at the same time.
1. Introduction Industrial development has made an enormous contribution to the economic growth process in China over the last several decades. How ever, the rise of manufacturing industries also has caused many envi ronmental concerns. One of the most severely issues is the soaring greenhouse gas emission caused by the excessive use of coal in the en ergy structure (Sun et al., 2019b; Wang et al., 2019a,2019b,2019c), which further leads to harmful impacts on climate change. According to the Global Energy and CO2 Status Report (2018)1 by International En ergy Agency, the CO2 released by China ranked the first in world and accounted for 28.5% of the global total of emission in 2018. The worsening climate accompanied by residents’ increasing de mand for a sustainable development leads to increasing attention on the mitigation of climate. Chinese people and government have noticed the past coal-fired power and heavy industrial growth model is not sus tainable and is harmful for both environment and climate. Therefore, China started to use a series of climate and environmental policies to achieve and accelerate decarburization, aiming to turn to a sustainable and inclusive low-carbon economy with higher quality, encourage the development of productive and higher-value industries and mitigate the
climate change at the same time (Green and Stern, 2017). For example, in 2006, China government has announced the “energy saving and emission reduction” policy in its 11th Five-year plan and set mandatory energy-saving targets to reduce energy consumption per unit of gross domestic product (GDP) by 20% average across provinces between 2006 and 2010. However, policy intervention into environment and climate has its benefits and costs. First of all, it will increase environmental compliance costs, which further leads several consequences. For example, the wellknown ‘pollution haven hypothesis’ argues some firms from dirty or energy-intensive industries prefer to locate in places with low environ mental standards. Hence, the environmental regulation and climate polices might exert negative effects on local productivity, which also gets support from empirical evidence (For example, Lanoie et al., 2008; Ricci, 2007; Greenstone, 2002; Shen et al., 2019). What is more, the Green Paradox literature states that some climate policies may accel erate the extraction of the fossil fuels and lead to climate change since firms expect stricter regulation in the future and speed up the extraction in now days (Sinn, 2008; Van der Ploeg and Withagen, 2012). On the other hand, we cannot deny the benefits of policy intervention. Not only because these regulation is beneficial to a sustainable development,
* Corresponding author. E-mail address: [email protected] (S. Wang). 1 See https://naturalsciences.ch/service/publications/100481-global-energy-co2-status-report. https://doi.org/10.1016/j.jenvman.2020.110148 Received 28 October 2019; Received in revised form 16 December 2019; Accepted 14 January 2020 Available online 24 January 2020 0301-4797/© 2020 Elsevier Ltd. All rights reserved.
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dominance of such a kind path-dependent process may lead to regions being constrained knowledge and capabilities among industries and prevent regions from opening new paths of development. Thus, it is important to understand the factors beyond the base technological and industrial structure, such as innovation policies and industrial in terventions, which can alter the technological trajectories and affect new industrial development paths. In more recent studies, some scholars have proved that extra-regional linkages and policies can affect regional industrial diversification. For instance, Uhlbach et al. (2017) find that EU Framework Programs improves the probability of new specializa tions of regions by compensating for a lack of related capabilities knowledge. However, they focus on the relatedness density of industries and estimate how policy can help regions to enter less related activities and achieve path-breaking development trajectories. The impacts of policy on industrial diversification and upgrade in terms of the complexity remain vague and unclear. To fill this gap, this study investigates the impacts of “energy saving and emission reduction” policy on industrial performance, with a particular focus on complexity. We start with analyzing whether more complex industry helps energy consumption reduction and find more complex industrial structure contribute to the coal consumption reduc tion. Hence, developing more complex industry provides a potential way to reduce the coal consumption and contribute to the climate. Then, we estimate how the emission targets affects province’s attractiveness to new industries. In general, “energy saving and emission reduction” policy reduce the probability of branching into new industries. This negative effect is weaker for more complex industries. This result in dicates environmental regulation stringency is harmful in attracting new industries to settle in one region, which supports the pollution heaven hypothesis that assets firms prefer to locate in places with lower envi ronmental standards to reduce higher environmental compliance costs. In the last robustness check section, we use the added value of output and the number of unprofitable firms of certain industries in one frim as proxy for industrial performance to investigate the effect of regulation. Emission reduction targets lowers the productivity and profitability of an industry in general. Theoretically, only for industry with extremely high complexity, the emission reduction targets exerts a positive effect rather than a negative one. In doing so, we contribute to the literature from the following perspective. First, we move beyond the previous literature on environ mental regulation on economic output and industrial development from a macro-level perspective and investigate the effect of environmental regulation from industrial diversification perspective. Understanding the link between policy shocks and different industries has great im plications for firms’ location choice and policymakers when analyzing the cost and benefit of environmental regulation. Second, the evolu tionary economic literature mostly investigates the relatedness density of industries. Our focus on economic complexity of industries extends the industrial diversification literature which mainly pays attention to industrial relatedness. Third, though recent studies have estimated how policy can help regions to enter less related activities and achieve path-breaking development trajectories. However, they usually analyze from a subsi dized policy perspective, relatively few studies have investigated the progress of industrial diversification is altered if a policy can brought abatement cost and innovation stimulus at the same time. The remainder of this paper is organized as follows. Section 2 de scribes the data, index construction and model specification. Section 3 presents and discuss the empirical results. Section 4 concludes.
more importantly, it is also potentially productivity enhancing by stimulating cleaner, more advanced and more efficient technologies and low-carbon innovations (Ricci, 2007; Porter, 1991; Guo et al., 2018; Zhu et al., 2019; Murty and Kumar, 2003; Testa et al., 2011). Therefore, it is effective in industrial upgrading and resources utilization the long run. In the stream of literature focusing on energy saving and emission reduction policies, the result is not consistent as well. Yao et al. (2019) and Yang et al. (2017) estimated the impact of China’s carbon intensity target and found it hinder the promotion the green production efficiency of industry and such an impact strengthens over time. The results of Li et al. (2012) indicated CO2 emission reduction policies had negative effects on GDP. Albrizio et al. (2017) found that the energy reduction policy turned the input factors from production end to the emission reduction end, which is harmful in the increase of green production performance. Shao et al. (2019b) adopt difference-in-differences (DID) method and find the implementation of energy intensity constraint policy has a significantly negative effect on total factor energy efficiency growth of sub-sectors with higher levels of energy intensity. To the contrary, Yang and Yang (2016) proved that the total factor productivity enhances after energy saving and emission reduction policies. Similar results are also found by Zhang and Choi (2013). However, these studies have merely focused on the impact of envi ronmental regulation on economic output and industrial development from a relative macro-level perspective, with relatively little attention paid to industry-specific effect. Since each industry varies not only in their technological composition but also in their values, their contri bution to emission reduction and economic growth is various as well. Therefore, it is worthwhile for provincial leaders and manufacturers to understand how these regulations affect industries distinctively in terms of their heterogeneity. In addition, due to the benefits and costs brought by climate policy and environmental regulation, policy makers always face the dilemma about the trade-off economic development and the mitigation of climate change. Therefore, understanding the industryspecific effect of emission reduction policy not only helps to design a more effective policy to achieve industrial development strategy, but also provides a potential way to achieve economic growth while reduce the emission of Greenhouse gases at the same time. With this borne in mind, this study will draw upon the recent eco nomic evolutionary literature on knowledge complexity and economic complexity (Hidalgo and Hausmann, 2009; Balland and Rigby, 2017; Balland et al., 2019) to investigate the impacts of “energy saving and emission reduction” policy on industrial performance, with a particular focus on complexity. Economic evolutionary literature has identified that the probability of branching into a new activity and the industrial diversification in creases when the number of related activities in a certain region is higher (Boschma and Frenken, 2011; Neffke et al., 2011). This statement is relied on the idea of principle of relatedness (Hidalgo et al., 2007), which contends that regions are more likely to branch into an industry space related to the existing industrial structure with strong knowledge and capabilities base. A number of empirical studies also provide evi dence to support this argument (Boschma and Capone, 2015; Balland, 2017; Guo et al., 2018; Zhu et al., 2017). Considering regional knowl edge bases and structures is difference only in their technological composition but also in their values, recent studies also pay a lot of attention on the complexity of economic activities to understand the process of innovation, industrial and economic development (Hidalgo and Hausmann, 2009; Balland and Rigby, 2017). As a crucial dimension of tacit knowledge, complexity is vital to generate competitive advan tage for regions (Asheim and Gertler, 2005; Kogut and Zander, 1992). More complex economic activities with higher exclusivity and value of knowledge are more difficult to be replicated, transferred and imitate, (Simon, 1991). Therefore, they are beneficial to the economic perfor mance in the long-term (Hidalgo and Hausmann, 2009; Balland and Rigby, 2017)). The traditional industrial diversification literature suggests the
2. Data and model specification The data used in the empirical analysis are from four major sources. The first source is retrieved from CSMAR Database, which is used to construct industrial structure related variables and measure the industry performance. CSMAR Database is the one of the most widely used 2
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database for Chinese business and economic research, covering various economic and financial information at different regions. The sources of these data are collected form statistic yearbook, financial statements, and government official documents and so on. The second source is emission reduction variables in each provinces and autonomous regions from the 11th Five-year plan of each province. The third source is province market-fundamentals obtained from China Province Statistical Yearbooks.
available and haDensity to replicable in other provinces. Therefore, this industry is more complex. To score high in complexity, province need to be specialized in non-ubiquitous industries. The formal specification the diversity of a province (Kp;0 ) and the ubiquity of an industry (Ki;0 ) is as follows: X Diversity ¼ Kp;0 ¼ Mi;p (3) p
Ubiquity ¼ Ki;0
2.1. Industrial structure related variables
X ¼ Mi;p
(4)
i
By combining diversity and ubiquity, ECI of provinces and ICI of industries can be calculated over a number of iterations (n):
We follow Hidalgo et al. (2007) and Hidalgo and Hausmann (2009) to calculate 1). Revealed comparative advantage (RCA) to reflect whether a province specialize in a certain industry 2). Relatedness density (DENSITY) to measure distance between a certain industry and a province’s existing industrial structure3). Industry Complexity Index (ICI) reveals the knowledge intensity of an industry and 4). Economic Complexity Index (ECI) to presents knowledge accumulated and shown in economic activities in a province. The RCA concept and measurement have been used in many different contexts, such as patents, publications, occupations and industries (Hi dalgo et al., 2018). The revealed comparative advantage (RCA) of each three-digit industry in each province is calculated as follows: �P industryi;c;t industryi;c;t �Pi P RCAi;c;t ¼ 1 if P >1 (1) industry i;c;t c c i industryi;c;t
ECIp ¼ ECIp;n ¼
ICIi ¼ ICIi;n ¼
1 X Mi; ICIi;n kp;0 i
1 X Mi;p ECIp;n ki;0 p
1
1
(5) (6)
To reflect the relative complexity level of industry i to province p’s specialization basket, we calculate RCI which equals the ratio of ðICIi þ1Þ to ðECIp þ1Þ 2: � � RCIi;p ¼ ðICIi þ 1Þ ECIp þ 1 (7) When RCIi;p is larger than 1, that means that industry i is more complex than province p current average complex level, and vice versa.
where industryi;c;t is the output of industry i in province p in time t. Equation (1) shows that an industry i has revealed comparative advan tage (RCA ¼ 1) in province p if the proportion of industry i in the province’s product portfolio is above the average share of the industry to the country’s total. A bipartite network that combines a province and a industry can be presented as a province-industry adjacency matrix Mi;p , where Mi;p ¼ 1 if province p has RCA in industry i and 0 otherwise. Then, the industry relatedness density of a specific industry i in province p in year t is specified as follows. P j RCAj;p;t φi;j;t P Densityi;p;t ¼ (2) j φi;j;t
2.2. Environmental regulation Since 1953, China’s government started to develop very detailed guidelines for country wide social and economic development every 5 years, which is called Five-Year Plan. Before the 10th Five-Year Plan (2001–2005), the previous plans focused on the economic growth with relatively few attention to the environmental issues. Though 10th FiveYear included the concerns of environmental issues into its plan, it did not set a province-level reduction target and implementation is not very effective. Generally, the public and academic regard 11th Five-year plan is a start of official “energy saving and emission reduction” policy. What is more, it is the first time that the central government incorporate the environmental protection as one determinates of the promotion of local leader. To mitigate climate change and reduce the coal use, 11th Five-year plan also required energy consumption per unit of gross domestic product (GDP) reduced by 20% between 2006 and 2010. The china state council issued “Reply to pollution control plan during 11th Five-year plan” in 2006, which handed down the national goal to reduction tar gets in provincial level. Therefore, each provinces and autonomous re gions have set the individual regional emissions targets based on their industrial structure base and development strategy. In the next two Five-year plan (12th Five-year plan (2011–2015) and 13th Five-year plan (2016–2020), the “energy saving and emission reduction” policy continued a significant development strategy, aiming to turn the old heavy industry growth model to a sustainable environ mental friendly and low-carbon economy. In this paper, we use regional emissions reduction targets (ERT) as a proxy for stringency of the emission reduction target. Considering the overlap of the industrial data and climate policy data, we only employ data from the 11th Five-year plan of each province.3 The details of the
φi;j;t is the relatedness between industries i and j, which is defined as: φi;j;t ¼ minfPðRCAi;p;t ¼ 1jRCAj;p;t ¼ 1Þ; PðRCAj;p;t ¼ 1jRCAi;p;t ¼ 1Þg. The relatedness concept is based on the idea that if two industries are more related, they demand similar inputs, including capital, infra structure, and knowledge and so on. A higher value of φi;j;t suggests two industries are more likely to be produced together in the same province. Densityi;p;t is the average RCA of all other existing industries in a given province weighted by their relatedness with this specific industry, which measures the distance between a certain industry and existing industrial structure of a province. An industry with a higher density in a given province indicates that this industry is surrounded by many well developed similar industries. We then apply the method of reflection (Hidalgo and Hausmann, 2009) to calculate the Economic Complexity Index (ECI) of provinces which reflects knowledge accumulated and shown in economic activ ities in provinces, and Industry Complexity Index (ICI) of industries, which measures the knowledge intensity of an industry. By construction, complexity is determined by two factors: 1). the diversity of a province (the number of industries that a province has specialized in (RCA ¼ ¼ 1) and 2). the ubiquity of an industry (the number of provinces that have RCA in a specific industry). Higher diversity means a province are specialized in numerous in dustries, indicating this province is diversified and therefore tends to be more complex in its industrial structures. Low ubiquity demonstrates that if only very few provinces has RCA in a specific industry, then knowledge and capabilities needed to develop this industry is rarely
2 ICI and ECI are standardized index which range from 1 to 1. Considering the existence of negative value, we use ðICIi þ1Þ=ðECIp þ1Þ to reflect the relative complexity level. 3 Data are from the official website National Development and Reform Commission: http://www.ndrc.gov.cn/fzgggz/hjbh/hjjsjyxsh/201106/t2011 0610_417635.html.
3
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target for each province are shown in Table 1.
is year fixed effects to represent business cycles respectively. In addition, we include some province-specific control variables (Xp;y 1 ): ECI which represents economic complexity of a province; GDP which represents the total GDP level of a province; FDI which indicates Foreign Trade Volume; POP which is the total population. These var iables enable us to capture the variations across provinces in terms of their economic development stage, openness, size and so on.
2.3. Control variables As for control variables, we include GDP per capita, total population (POP), Foreign Trade Volume (FTV). These data are gained from Sta tistical Yearbook of each province. Data used to calculate these variables are collected from the China Province Statistic Yearbooks. These control variables are from the China province Statistic Yearbooks. The descriptive statistics of all variables are shown in Table 24.
3. Empirical results In this section, we start with analyzing the relationship between complexity and coal consumption, to get a general idea about whether more complex industrial structure help energy consumption reduction. Then, we estimate how the emission reduction targets (ERT) affects province’s attractiveness to new industries. In the last robustness check section, we use several variables as proxy for industrial productivity and profitability to investigate the effect of ERT industrial performance.
2.4. Model specification We use a linear probability model with fixed effects to predict the entry probability of specific industries in Chinese province. The speci fication of the entry model is as follows: Entryi;p;y ¼ α þ β1 *Density i;p;y þ β4 *ERTP *Density i;p;y
1
1
þ β2 *RCIi;p;y
1
þ β3 *ERTP
þ β5 *ERTP *RCIi;p;y
1
þ β*Xp;y
1
3.1. Complexity and coal consumption
þ δip þ λy (8)
Before proceeding to the regression results, we first describe the re lationships between complexity and the coal consumption, in both in dustrial and province level. Fig. 1 displays the scatter plot of complexity and the ratio of coal consumption to the output added value (Coal consumption) at 2-digit industrial level.5 Obviously, there is a negative relationship between coal consumption per added value and complexity, indicating more complex industries indeed consume less coal when producing an addi tional value of output. This is similar to what Sun et al (2019a) find in their study, which indicates emerging service sectors improve CO2 emission efficiency, but the traditional sectors hinder the efficiency improvement. To some extent, this preliminary results suggest devel oping more complex industry is a way to reduce the coal consumption. Fig. 2 exhibits the scatter plot of knowledge complexity and the ratio of coal consumption to the regional GDP at province level. We can clearly see that coal consumption per GDP and complexity is negatively correlated. Such a result suggest having more complex knowledge embedded in industrial structure is more eco-friendly and reduces the coal consumption to per unit of GDP, which further suggests developing more complex industry probably can reduce the coal consumption at province level and contributes to the climate mitigation. Table 3 shows how the improvement of knowledge complexity of a province contributes to the coal consumption. Following Shao et al. (2019a), we use the ratio coal consumption to GDP, coal consumption per capital and the total coal consumption to demonstrate the coal consumption level of a province. No matter in which coal consumption definition, our results show the growth rate of complexity (KCI Growth Rate) decreases the coal consumption to GDP, coal consumption per capital and the total coal consumption. Overall, the result confirms that more complex industrial structure contributes to the coal consumption reduction.
where Entryi;p;y equals 1 only when province p does not have RCA in industry i for years before y and gains RCA for after years y, which is used to demonstrate whether an industry is an newly developed industry to a province. This two-year smoothing is used to avoid RCA fluctuation around the threshold. Density i;p;y 1 is industrial relatedness density, measureing the distance between industry i and the other existing in dustries in province p at year y 1. RCIi;p;y 1 represents the difference in industry complexity of industry i and economic complexity of province p at year y 1. ERTp is provincial emission reduction targets in the 11th five-year plan. Considering the potentially heterogeneous effects of emission reduction targets on different industries, we also include the cross term of ERTp and RCIi;p;y 1 (ERTp *RCIi;p;y 1 ) to examine the whether province with stricter environmental regulation are more likely to gain more complex industries, and the cross term of ERTp and Densityi;p;y 1 Table 1 Provincial energy intensity reduction targets for the 11th FYP period. Province
Target (%)
Province
Target (%)
Province
Target (%)
Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang
20 20 20 22 22
Anhui Fujian Jiangxi Shandong Henan
20 16 20 22 20
Sichuan Guizhou Yunnan Tibet Shan’xi
20 20 17 12 20
20 20 20 20 20 20
Hubei Hunan Guangdong Guangxi Hainan Chongqing
20 20 16 15 12 20
Gansu Qinghai Ningxia Xinjiang
20 17 20 –
3.2. The effect of environmental regulation on the industrial diversification
(ERTp *Densityi;p;y 1 ) to test how climate policy affects the pathdependent process. δip represents province-industry fixed effect to con trol the unobserved factors changes across industry-province pair, and λy
Table 4 shows the main result regarding the relationship between emission target, complexity and industrial diversification. Column (1) shows the baseline regression without considering the effect of emission targets. Specifically, we can see that the coefficient of relatedness density (Density) is significantly positive, indicating density increase the probability of branching into new industries in a province.
4
The period selection period is based on the data availability. The industrial data of each province is from 1999 to 2011. Therefore, the province-level control variables used in the analysis are also from 1999 to 2011. Consid ering the overlap of the industrial data and climate policy data, we only employ data from the 11th Five-year plan of each province. Therefore, the period of climate policy data is from 2006 to 2010. In our empirical analysis, when the climate policy is considered, the analyzed data is from 2006 to 2011(climate policy is lagged by one period).
5 Since we can only get the industrial coal consumption data at 2-digit level, here we report the scatter plot at 2-digit level. In the following industrial diversification level analysis, we conduct the empirical analysis at 3-digit level.
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Table 2 Descriptive statistics of variables. Variable
Definitions
Obs
Unit
Level
Mean
Std. Dev.
Min
Max
RCA DENSITY RCI No. Firms
RCA Related Density Ratio of Complexity The number of firms in an industry The number of un-profitable firms in an industry The output of an industry Add value of output of an industry
54,325 54,325 54,325 54,340
– – – 1 unit
Province_ industry Pair
0.35 28.66 1.04 63.30
0.48 13.33 0.10 164.60
0.00 0.57 0.79 0.00
1.00 100.00 1.35 8994.00
54,338
1 unit
10.05
23.08
0.00
762.00
54,338 45,357
10,000 Yuan 10,000 Yuan
7203134.00 903724.10
25100000.00 3241608.00
0.00 6079106.00
842000000.00 92200000.00
ICI Coal_Indsutry
Industry complexity index Coal consumption of an industry
2087 434
– 10,000 Tons
0.01 6118.45
0.02 19756.02
0.04 1.39
0.06 170744.20
GDP
Gross Domestic Product
403
7702.23
8322.26
105.61
53210.28
FTV POP
Foreign Trade Volume Total population (10,000 person) Economic complexity index Total Coal Consumption Total Coal Consumption/GDP
403 403
100,000,000 Yuan 1000 US Dollars 10,000 person
2829465.00 4185.93
7354474.00 2642.18
39.84 256.00
55000000.00 10504.85
0.03 8757.80 1.59
0.10 7300.22 1.33
0.21 179.00 .146
0.24 38921.00 9.13
Total Coal Consumption/Total population
386
2.28
1.86
.23
13.98
No.Unprofitable Output Added Value
ECI Coal_Province Coal Consumption per GDP Coal consumption per capital
403 397 386
Industry(3 digit) Industry(2 digit)
Province
– 10,000 Tons Tons/10,000 Yuan Tons/person
Fig. 1. Industrial Complexity and Coal Consumption/Output Added Value (2 digit).
This result is consistent with the previous relatedness and industrial diversification literature, which argues industries more related to the existing industries of a region are more likely to be well developed in that region (Boschma and Capone, 2015; Balland et al., 2019; Guo and He, 2017; Zhu et al., 2017). Column (2) shows the overall effects of ERT policy. We can see that emission reduction target plays a significant negative role in the entry probability of new industries after considering the fixed effects. In the other words, stricter regulation discourage new industries to settle in one province. This is one of the negative impacts brought by the mission reduction target since it is harmful to developing diversified industries. Column (3) to Column (5) shows the heterogeneous effects of in dustrial land subsidy on industries with different levels of related den sity and complexity. In Column (3), we find that interaction term
ERT�RD is significantly positive. Stricter reduction targets reduces the probability of branching into new industries, but this negative effect is weakened for industries that is closely related to the current industrial structure of a province. This is not a good news for regions since such a result demonstrates that ERT policy will strengthen the path-dependent process and is not beneficial for industrial diversification. In Column (4), ERT�RCI has a significantly positive effect on entry probability. For industries with higher complexity, the negative effect of ERT on entry probability is weaker. In the full model (Column (5)) with both the cross term of ERT�RD and ERT�RCI, our results are still robust. When RCI is larger than 1.8, ERT policy environmental regulation’s effect turns to positive. However, from Table 2, we can see that the maximum value of RCI is 1.35. Therefore, this threshold is only a theoretical value. Taking together, stricter environmental regulation is not helpful in 5
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Fig. 2. Knowledge complexity and coal consumption/GDP
3.3. Robustness check
Table 3 Province-level analysis: The effect of complexity on province coal consumption. Variables
KCI Growth Rate Log(GDP) Log(FTV) Log(Population) Coal Consumption/ GDP Coal Consumption per capita Log(Coal Consumption) Constant Observations Year FE Province FE Adjusted Rsquared Within R-squared
(1)
(2)
(3)
Coal Consumption per GDP
Coal Consumption per capita
Log(Coal Consumption)
0.00141*** (0.00009) 0.03944 (0.10743) 0.05460 (0.03376) 0.34117* (0.19081) 0.88167*** (0.04140)
0.00197*** (0.00023) 0.28761 (0.18376) 0.03456 (0.03970) 0.13943 (0.33174)
0.00104*** (0.00020) 0.03891 (0.07613) 0.00324 (0.01512) 0.27509 (0.16569)
1.12269*** (0.06838) 3.59977** (1.37278) 344 YES YES 0.90899
0.84590 (2.05959) 344 YES YES 0.95433
0.84571*** (0.06291) 3.20426** (1.34441) 343 YES YES 0.96450
0.91323
0.95646
0.96616
To further test the effect of environmental regulation on industrial performance, we use other dependent variables a robustness check. We first use the total industrial output and added value of industrial output to test the effect of ERT on industrial productivity. Then, we employ the number of the firms that does not make profits in each province, and its ratio to the total number of firms in one industries as proxies for in dustrial profitability. The results of productivity and profitability are shown in Table 5 and Table 6 respectively. Column (1) and Column (3) in Table 5 shows the overall effects of ERT on the total output and added value of industrial output. When not considering the heterogeneity in industries, the coefficient of environ mental regulation is significantly negative in both models, which means environmental regulation lowers the productivity of an industry. In Column (2) and Column (4), the significantly positive coefficient of ERT*RCI suggests negative effect of ERT on industrial performance is weaker for more complex industries. Specifically, if RCI is larger than 1.48, ERT can improve the output rather than exerting a negative effect. This figure is 1.47 for added value of industrial output. Table 6 shows the effects of ET on profitability of industries. The overall effect of ET is positive (Column (1) and (3)). That is to say, ET will increase the number of firms that do not make profit in an industry and also the non-profit firm’s ratio in a given industry. The coefficient of ET*RCI is significantly negative(Column(2) and (4)), indicating if an industry is more complex, this industry will less negatively affected by regulation and won’t have so many unprofitable firms. When RCI is larger than 1.44, regulation reduce the number of unprofitable firms on the contrary. To sum up, results shown in Tables 5 and 6 further confirm the finding in previous part: ET have a negative effect overall but is less harmful to industries with higher complexity.
Note: All independent variables are mean-centered and lagged by one period; Robust standard errors are in parentheses and are clustered at the province level; ***p < 0.01, **p < 0.05, *p < 0.1.
developing new industries, especially for industries that is less related to the existing specialized industries. However, for industries with higher level of complexity, this effect of ERT is less harmful. Theoretically, for industry with extremely high complexity, the emission reduction targets exert a positive effect rather than a negative one. These results are generally in line with the environmental regulation literature suggesting regulation crowd out the low-end industries and helps the industrial upgrading in the long run.
4. Conclusion China has witnessed a rapid industrial development the last several decades. The progress in manufacturing industries makes an enormous contribution to the economy, but at the same time leads to some climate concerns due to excessive use of coal by heavy industries. With the 6
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Table 4 The effect of environmental regulation. Variables
(1)
(2)
(3)
(4)
(5)
0.00224*** (0.00073) 0.01957 (0.07349) 0.00833*** (0.00250)
0.00165** (0.00075) 0.08706 (0.07951) 0.00941*** (0.00247) 0.00005** (0.00002)
0.00196*** (0.00074) 0.17217* (0.09903) 0.01622*** (0.00406)
0.00038 (0.00086) 0.33375*** (0.10201) 0.03465*** (0.00526) 0.00015*** (0.00003) 0.01925*** (0.00323) 0.02825 (0.04461) 0.01135 (0.00768) 0.09972 (0.06833) 0.35629 (0.73128) 16,710 YES YES 0.01814 0.01890
Entry Density RCI ERT
0.00213*** (0.00071) 0.00947 (0.06837)
ERT*DENSITY ERT*RCI Log(GDP) Log(FTV) Log(Population) Constant Observations Year FE Province-Industry FE Adjusted R-squared Within R-squared
0.01279 (0.04360) 0.01278* (0.00762) 0.03500 (0.06808) 0.37047 (0.72852) 17,109 YES YES 0.01416 0.01473
0.00358 (0.04463) 0.01331* (0.00767) 0.02658 (0.06837) 0.48230 (0.73056) 16,710 YES YES 0.01513 0.01577
0.00660*** (0.00223) 0.01834 (0.04484) 0.01344* (0.00768) 0.05176 (0.06816) 0.29685 (0.73420) 16,710 YES YES 0.01562 0.01633
0.01410 (0.04485) 0.01255 (0.00767) 0.02648 (0.06834) 0.61823 (0.73428) 16,710 YES YES 0.01556 0.01627
Note: All independent variables are mean-centered and lagged by one period; Robust standard errors are in parentheses and are clustered at the province and industry level; ***p < 0.01, **p < 0.05, *p < 0.1. Table 5 The effect of ERT on productivity. Variables
(1)
(2)
(3)
Log(Output) Log(Output) RCI ERT ERT*RCI KCI Log(GDP) Log(FTV) Log(Population) Log(Added Value) Constant Observations Year FE Province-Industry FE Adjusted R-squared Within R-squared
0.34695*** (0.01076) 1.99289 (1.40786) 0.11777*** (0.02647)
(4)
Log(Added Value)
5.47554*** (1.59934) 2.06566*** (0.28205) 0.01631 (0.04146) 1.76479*** (0.24028)
0.33183*** (0.01092) 8.49925*** (1.45511) 0.55756*** (0.04208) 0.37551*** (0.02694) 1.01755 (1.63597) 0.74887*** (0.26850) 0.01094 (0.04191) 0.27968 (0.24483)
7.16694* (4.09773) 26,023 YES YES 0.41234 0.41261
13.19406*** (4.03360) 26,023 YES YES 0.43207 0.43235
14.62116*** (2.89344) 0.15561*** (0.02655) 32.15850*** (3.39743) 2.50830*** (0.61585) 0.11228 (0.09669) 4.37557*** (1.18662) 0.49779*** (0.01090) 35.68830*** (12.16868) 17,308 YES YES 0.94160 0.94164
19.48646*** (2.79538) 0.73371*** (0.04198) 0.49901*** (0.02777) 18.89839*** (3.33617) 0.76403 (0.58427) 0.14924 (0.09416) 6.48160*** (1.20686) 0.47923*** (0.01087) 18.90067 (12.05460) 17,308 YES YES 0.94436 0.94440
Note: All independent variables are mean-centered and lagged by one period; Robust standard errors are in parentheses and are clustered at the province and industry level; ***p < 0.01, **p < 0.05, *p < 0.1.
increasing demand for quality of life and importance of sustainable development, government has applied an array of mitigating measures to reduce CO2 emission and cope with the climate change, one of which is “energy saving and emission reduction” policy since its 11th Five-year plan. This paper attempts to estimate impact of this “energy saving and emission reduction” policy from industry complexity perspective. Our result shows: Overall, more complex industrial structure contributes to the coal consumption reduction, which suggests developing more complex
industry is a way to reduce the coal consumption and mitigate the climate change. In general, emission reduction target policy reduces the probability of branching into new industries. What is more, emission reduction target policy has a negative effect on the productivity and profitability of an industry as well. Compared with lower complex in dustry, the higher complex industry is less harmed. Theoretically, for industry with extremely high complexity, the emission reduction targets exert a positive effect rather than a negative one. Our contribution to the literature is threefold. First, we move beyond 7
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Journal of Environmental Management 260 (2020) 110148
policies, such as subsidies for renewable energy, environmental resource tax, emissions trading system and so on, is necessary for designing a more effective strategy to achieve a better industrial performance and alleviate environmental issues at the same time.
Table 6 The effect of ET on industrial profitability. Variables
(1)
(2)
Log(No.Unprofitable Firms) Log(No. Unprofitable Firms) RCI ERT ERT*RCI KCI Log(GDP) Log(FTV) Log (Population) Unprofitable Ratio Constant Observations Year FE ProvinceIndustry FE Adjusted Rsquared Within Rsquared
0.14638*** (0.00787)
0.14100*** (0.00783)
0.53169 (0.51434) 0.01248*** (0.00343) 0.69011 (0.60231) 0.11651 (0.08116) 0.06495*** (0.01256) 0.77538*** (0.11928)
1.27536** (0.51882) 0.06432*** (0.00578) 0.04444*** (0.00390) 0.11548 (0.60370) 0.28203*** (0.08283) 0.06564*** (0.01250) 0.95995*** (0.12028)
7.06438*** (1.49202) 26,023 YES YES
(3)
(4)
Unprofitable Ratio
Author contributions 0.38645** (0.15457) 0.00656*** (0.00130)
Dong ZYZ and Wang SJ designed the research; Dong ZYZ performed experiments and computational analysis; Dong ZYZ drafted the paper; Dong ZYZ and Chen WQ contributed to the interpretation and the preparation of the manuscript, and Dong ZYZ and Wang SJ contributed to the final draft of the manuscript.
6.39777*** (1.50600) 26,023 YES YES
0.62673*** (0.16346) 0.12219*** (0.02395) 0.01404*** (0.00419) 0.05648* (0.03154) 0.10202*** (0.01311) 0.57518 (0.43620) 22,836 YES YES
0.53257*** (0.15636) 0.01678*** (0.00215) 0.00858*** (0.00121) 0.51690*** (0.16365) 0.09023*** (0.02425) 0.01301*** (0.00422) 0.02215 (0.03137) 0.09840*** (0.01311) 0.41756 (0.43918) 22,836 YES YES
0.20286
0.20715
0.13091
0.13396
References
0.20323
0.20755
0.13137
0.13445
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Declaration of competing interest None. Acknowledgments This research was funded by the National Natural Science Founda tion of China (Grant Nos. 71473222, 71874156), the Fundamental Research Funds for the Central Universities (19lgzd09), Guangdong Special Support Program snf Pearl River S&T Nova Program of Guangzhou (201806010187).
Note: All independent variables are mean-centered and lagged by one period; Robust standard errors are in parentheses and are clustered at the province and industry level; ***p < 0.01, **p < 0.05, *p < 0.1.
the previous literature on environmental regulation on economic output and industrial development from a macro-level perspective and inves tigate the effect of emission reduction policy from relative micro in dustrial perspective. Second, focusing on economic complexity of industries extends the industrial diversification literature which mainly pays attention to industrial relatedness Third, we aim to contribute to the industrial diversification literature by analyzing whether the regional industrial branching process can be influenced by uncertainty induced by policy interventions. Our finding indicates this “emission reduction” policy is harmful in attracting new industries to settle in one region and has a negative effect on the productivity of an industry, which supports the pollution heaven hypothesis that asserts firms prefer to locate in places with lower envi ronmental standards to reduce higher environmental compliance costs. As more complex industrial structure contribute to the coal consumption reduction and economic growth as well, it is extremely of importance to attract and develop more complex industries in one region. Considering the emission reduction targets currently has negative effect on all in dustries (though more complex industry is less harmed), it is also necessary to provide subsidies though other policies, such as specific tax preference policy for specific industries, to offset negative effect of the regulation and help region to attract these industries to settle down. However, due to the data limitation, our results is a short-run anal ysis. Therefore, whether this emission reduction policy can have positive effect on industry structure upgrade in the long term is still an open question and need further analysis. It will be a promising future research direction to evaluate both the benefit and the cost of such a policy on climate and industrial development in a long run. In addition, we only estimate the effect of emission reduction policy on industrial perfor mance. However, with the increasing importance role of environment in the society development, various command and control policy and market-based policy has been adopted by Chinese government. There fore, the comparison of efficiency of different kinds of environmental 8
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Journal of Environmental Management 260 (2020) 110148 Wang, S.J., Fang, C., Sun, L., Su, Y., Chen, X., Zhou, C., Feng, K., Hubacek, K., 2019a. Decarbonizing China’s urban agglomerations. Ann. Assoc. Am. Geogr. 109 (1), 266–285. Wang, S.J., Wang, J.Y., Fang, C.L., Li, S.J., 2019b. Estimating the impacts of urban form on CO2 emission efficiency in the Pearl River Delta, China. Cities 85, 117–129. Wang, S.J., Zeng, J., Liu, X., 2019c. Examining the multiple impacts of technological progress on CO2 emissions in China: a panel quantile regression approach. Renew. Sustain. Energy Rev. 103, 140–150. Yang, M., Yang, F., 2016. Energy-efficiency policies and energy productivity improvements: evidence from China’s manufacturing industry. Emerg. Mark. Finance Trade 52 (6), 1395–1404. Yang, Z., Fan, M., Shao, S., Yang, 2017. Does carbon intensity constraint policy improve industrial green production performance in China? A quasi-DID analysis. Energy Econ. 68, 271–282. Yao, Y., Jiao, J., Han, X., Wang, C., 2019. Can constraint targets facilitate industrial green production performance in China? Energy-saving target vs emission-reduction target. J. Clean. Prod. 209, 862–875. Zhang, N., Choi, Y., 2013. Total-factor carbon emission performance of fossil fuel power plants in China: a meta frontier non-radial Malmquist index analysis. Energy Econ. 40, 549–559. Zhu, J., Fan, Y., Deng, X., Xue, 2019. Low-carbon innovation induced by emissions trading in China. Nat. Commun. 10 (1), 1–8. Zhu, S., He, C., Zhou, Y., 2017. How to jump further and catch up? Path-breaking in an uneven industry space. J. Econ. Geogr. 17 (3), 521–545.
Shao, S., Yang, Z., Yang, L., Ma, S., 2019b. Can China’s energy intensity constraint policy promote total factor energy efficiency? Evidence from the industrial sector. Energy J. 40 (4). Shen, J., Wang, S., Liu, W., Chu, J., 2019. Does migration of pollution-intensive industries impact environmental efficiency? Evidence supporting “Pollution Haven Hypothesis”. J. Environ. Manag. 242, 142–152. Simon, H.A., 1991. The architecture of complexity. In: Facets of Systems Science. Springer, Boston, MA, pp. 457–476. Sinn, H.W., 2008. Public policies against global warming: a supply side approach. Int. Tax Publ. Financ. 15 (4), 360–394. Sun, C., Ding, D., Fang, X., Zhang, H., Li, J., 2019a. How do fossil energy prices affect the stock prices of new energy companies? Evidence from Divisia energy price index in China’s market. Energy 169, 637–645. Sun, C., Ma, T., Ouyang, X., Wang, R., 2019b. Does service trade globalization promote trade and low-carbon globalization? Evidence from 30 countries. Emerg. Mark. Finance Trade 1–19. Testa, F., Iraldo, F., Frey, M., 2011. The effect of environmental regulation on firms’ competitive performance: the case of the building & construction sector in some EU regions. J. Environ. Manag. 92 (9), 2136–2144. Uhlbach, W.H., Balland, P.A., Scherngell, T., 2017. R&D Policy and Technological Trajectories of Regions: Evidence from the EU Framework Programmes. Van der Ploeg, F., Withagen, C., 2012. Is there really a green paradox? J. Environ. Econ. Manag. 64 (3), 342–363.
9
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Journal of Environmental Management journal homepage: http://www.elsevier.com/locate/jenvman
Research article
Emission reduction target, complexity and industrial performance Zhaoyingzi Dong a, Wenqiang Chen b, Shaojian Wang c, * a
School of Public Affairs, Zhejiang University, Zhejiang, China School of Accounting, Zhejiang University of Finance & Economics, Zhejiang, China c Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China b
A R T I C L E I N F O
A B S T R A C T
Keywords: Climate change “Emission reduction target” policy Complexity Industrial diversification 11th five-year plan China
To tackle climate change, Chinese government has applied an array of mitigating measures to reduce CO2 emissions. During the 11th Five-year Plan, Chinese government set the emission reduction target policy to reduce energy consumption per unit of gross domestic product (GDP) by 20%. This paper attempts to estimate the effect of this emission reduction target policy on industrial performance from complexity perspective. The result shows more complex industrial structure is related to less coal consumption. In general, emission reduction target policy has a negative effect on the probability of branching into new industries and lower the productivity and prof itability of a given industry. However, this negative effect is weaker for more complex industries. Only for in dustries with very high complexity, emission reduction target policy can improve the performance of an industry rather than exerting a negative effect. Our finding not only helps to design a more effective policy to achieve industrial development strategy, but also provides a potential way to achieve economic growth while reduce the emission of greenhouse gases at the same time.
1. Introduction Industrial development has made an enormous contribution to the economic growth process in China over the last several decades. How ever, the rise of manufacturing industries also has caused many envi ronmental concerns. One of the most severely issues is the soaring greenhouse gas emission caused by the excessive use of coal in the en ergy structure (Sun et al., 2019b; Wang et al., 2019a,2019b,2019c), which further leads to harmful impacts on climate change. According to the Global Energy and CO2 Status Report (2018)1 by International En ergy Agency, the CO2 released by China ranked the first in world and accounted for 28.5% of the global total of emission in 2018. The worsening climate accompanied by residents’ increasing de mand for a sustainable development leads to increasing attention on the mitigation of climate. Chinese people and government have noticed the past coal-fired power and heavy industrial growth model is not sus tainable and is harmful for both environment and climate. Therefore, China started to use a series of climate and environmental policies to achieve and accelerate decarburization, aiming to turn to a sustainable and inclusive low-carbon economy with higher quality, encourage the development of productive and higher-value industries and mitigate the
climate change at the same time (Green and Stern, 2017). For example, in 2006, China government has announced the “energy saving and emission reduction” policy in its 11th Five-year plan and set mandatory energy-saving targets to reduce energy consumption per unit of gross domestic product (GDP) by 20% average across provinces between 2006 and 2010. However, policy intervention into environment and climate has its benefits and costs. First of all, it will increase environmental compliance costs, which further leads several consequences. For example, the wellknown ‘pollution haven hypothesis’ argues some firms from dirty or energy-intensive industries prefer to locate in places with low environ mental standards. Hence, the environmental regulation and climate polices might exert negative effects on local productivity, which also gets support from empirical evidence (For example, Lanoie et al., 2008; Ricci, 2007; Greenstone, 2002; Shen et al., 2019). What is more, the Green Paradox literature states that some climate policies may accel erate the extraction of the fossil fuels and lead to climate change since firms expect stricter regulation in the future and speed up the extraction in now days (Sinn, 2008; Van der Ploeg and Withagen, 2012). On the other hand, we cannot deny the benefits of policy intervention. Not only because these regulation is beneficial to a sustainable development,
* Corresponding author. E-mail address: [email protected] (S. Wang). 1 See https://naturalsciences.ch/service/publications/100481-global-energy-co2-status-report. https://doi.org/10.1016/j.jenvman.2020.110148 Received 28 October 2019; Received in revised form 16 December 2019; Accepted 14 January 2020 Available online 24 January 2020 0301-4797/© 2020 Elsevier Ltd. All rights reserved.
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dominance of such a kind path-dependent process may lead to regions being constrained knowledge and capabilities among industries and prevent regions from opening new paths of development. Thus, it is important to understand the factors beyond the base technological and industrial structure, such as innovation policies and industrial in terventions, which can alter the technological trajectories and affect new industrial development paths. In more recent studies, some scholars have proved that extra-regional linkages and policies can affect regional industrial diversification. For instance, Uhlbach et al. (2017) find that EU Framework Programs improves the probability of new specializa tions of regions by compensating for a lack of related capabilities knowledge. However, they focus on the relatedness density of industries and estimate how policy can help regions to enter less related activities and achieve path-breaking development trajectories. The impacts of policy on industrial diversification and upgrade in terms of the complexity remain vague and unclear. To fill this gap, this study investigates the impacts of “energy saving and emission reduction” policy on industrial performance, with a particular focus on complexity. We start with analyzing whether more complex industry helps energy consumption reduction and find more complex industrial structure contribute to the coal consumption reduc tion. Hence, developing more complex industry provides a potential way to reduce the coal consumption and contribute to the climate. Then, we estimate how the emission targets affects province’s attractiveness to new industries. In general, “energy saving and emission reduction” policy reduce the probability of branching into new industries. This negative effect is weaker for more complex industries. This result in dicates environmental regulation stringency is harmful in attracting new industries to settle in one region, which supports the pollution heaven hypothesis that assets firms prefer to locate in places with lower envi ronmental standards to reduce higher environmental compliance costs. In the last robustness check section, we use the added value of output and the number of unprofitable firms of certain industries in one frim as proxy for industrial performance to investigate the effect of regulation. Emission reduction targets lowers the productivity and profitability of an industry in general. Theoretically, only for industry with extremely high complexity, the emission reduction targets exerts a positive effect rather than a negative one. In doing so, we contribute to the literature from the following perspective. First, we move beyond the previous literature on environ mental regulation on economic output and industrial development from a macro-level perspective and investigate the effect of environmental regulation from industrial diversification perspective. Understanding the link between policy shocks and different industries has great im plications for firms’ location choice and policymakers when analyzing the cost and benefit of environmental regulation. Second, the evolu tionary economic literature mostly investigates the relatedness density of industries. Our focus on economic complexity of industries extends the industrial diversification literature which mainly pays attention to industrial relatedness. Third, though recent studies have estimated how policy can help regions to enter less related activities and achieve path-breaking development trajectories. However, they usually analyze from a subsi dized policy perspective, relatively few studies have investigated the progress of industrial diversification is altered if a policy can brought abatement cost and innovation stimulus at the same time. The remainder of this paper is organized as follows. Section 2 de scribes the data, index construction and model specification. Section 3 presents and discuss the empirical results. Section 4 concludes.
more importantly, it is also potentially productivity enhancing by stimulating cleaner, more advanced and more efficient technologies and low-carbon innovations (Ricci, 2007; Porter, 1991; Guo et al., 2018; Zhu et al., 2019; Murty and Kumar, 2003; Testa et al., 2011). Therefore, it is effective in industrial upgrading and resources utilization the long run. In the stream of literature focusing on energy saving and emission reduction policies, the result is not consistent as well. Yao et al. (2019) and Yang et al. (2017) estimated the impact of China’s carbon intensity target and found it hinder the promotion the green production efficiency of industry and such an impact strengthens over time. The results of Li et al. (2012) indicated CO2 emission reduction policies had negative effects on GDP. Albrizio et al. (2017) found that the energy reduction policy turned the input factors from production end to the emission reduction end, which is harmful in the increase of green production performance. Shao et al. (2019b) adopt difference-in-differences (DID) method and find the implementation of energy intensity constraint policy has a significantly negative effect on total factor energy efficiency growth of sub-sectors with higher levels of energy intensity. To the contrary, Yang and Yang (2016) proved that the total factor productivity enhances after energy saving and emission reduction policies. Similar results are also found by Zhang and Choi (2013). However, these studies have merely focused on the impact of envi ronmental regulation on economic output and industrial development from a relative macro-level perspective, with relatively little attention paid to industry-specific effect. Since each industry varies not only in their technological composition but also in their values, their contri bution to emission reduction and economic growth is various as well. Therefore, it is worthwhile for provincial leaders and manufacturers to understand how these regulations affect industries distinctively in terms of their heterogeneity. In addition, due to the benefits and costs brought by climate policy and environmental regulation, policy makers always face the dilemma about the trade-off economic development and the mitigation of climate change. Therefore, understanding the industryspecific effect of emission reduction policy not only helps to design a more effective policy to achieve industrial development strategy, but also provides a potential way to achieve economic growth while reduce the emission of Greenhouse gases at the same time. With this borne in mind, this study will draw upon the recent eco nomic evolutionary literature on knowledge complexity and economic complexity (Hidalgo and Hausmann, 2009; Balland and Rigby, 2017; Balland et al., 2019) to investigate the impacts of “energy saving and emission reduction” policy on industrial performance, with a particular focus on complexity. Economic evolutionary literature has identified that the probability of branching into a new activity and the industrial diversification in creases when the number of related activities in a certain region is higher (Boschma and Frenken, 2011; Neffke et al., 2011). This statement is relied on the idea of principle of relatedness (Hidalgo et al., 2007), which contends that regions are more likely to branch into an industry space related to the existing industrial structure with strong knowledge and capabilities base. A number of empirical studies also provide evi dence to support this argument (Boschma and Capone, 2015; Balland, 2017; Guo et al., 2018; Zhu et al., 2017). Considering regional knowl edge bases and structures is difference only in their technological composition but also in their values, recent studies also pay a lot of attention on the complexity of economic activities to understand the process of innovation, industrial and economic development (Hidalgo and Hausmann, 2009; Balland and Rigby, 2017). As a crucial dimension of tacit knowledge, complexity is vital to generate competitive advan tage for regions (Asheim and Gertler, 2005; Kogut and Zander, 1992). More complex economic activities with higher exclusivity and value of knowledge are more difficult to be replicated, transferred and imitate, (Simon, 1991). Therefore, they are beneficial to the economic perfor mance in the long-term (Hidalgo and Hausmann, 2009; Balland and Rigby, 2017)). The traditional industrial diversification literature suggests the
2. Data and model specification The data used in the empirical analysis are from four major sources. The first source is retrieved from CSMAR Database, which is used to construct industrial structure related variables and measure the industry performance. CSMAR Database is the one of the most widely used 2
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Journal of Environmental Management 260 (2020) 110148
database for Chinese business and economic research, covering various economic and financial information at different regions. The sources of these data are collected form statistic yearbook, financial statements, and government official documents and so on. The second source is emission reduction variables in each provinces and autonomous regions from the 11th Five-year plan of each province. The third source is province market-fundamentals obtained from China Province Statistical Yearbooks.
available and haDensity to replicable in other provinces. Therefore, this industry is more complex. To score high in complexity, province need to be specialized in non-ubiquitous industries. The formal specification the diversity of a province (Kp;0 ) and the ubiquity of an industry (Ki;0 ) is as follows: X Diversity ¼ Kp;0 ¼ Mi;p (3) p
Ubiquity ¼ Ki;0
2.1. Industrial structure related variables
X ¼ Mi;p
(4)
i
By combining diversity and ubiquity, ECI of provinces and ICI of industries can be calculated over a number of iterations (n):
We follow Hidalgo et al. (2007) and Hidalgo and Hausmann (2009) to calculate 1). Revealed comparative advantage (RCA) to reflect whether a province specialize in a certain industry 2). Relatedness density (DENSITY) to measure distance between a certain industry and a province’s existing industrial structure3). Industry Complexity Index (ICI) reveals the knowledge intensity of an industry and 4). Economic Complexity Index (ECI) to presents knowledge accumulated and shown in economic activities in a province. The RCA concept and measurement have been used in many different contexts, such as patents, publications, occupations and industries (Hi dalgo et al., 2018). The revealed comparative advantage (RCA) of each three-digit industry in each province is calculated as follows: �P industryi;c;t industryi;c;t �Pi P RCAi;c;t ¼ 1 if P >1 (1) industry i;c;t c c i industryi;c;t
ECIp ¼ ECIp;n ¼
ICIi ¼ ICIi;n ¼
1 X Mi; ICIi;n kp;0 i
1 X Mi;p ECIp;n ki;0 p
1
1
(5) (6)
To reflect the relative complexity level of industry i to province p’s specialization basket, we calculate RCI which equals the ratio of ðICIi þ1Þ to ðECIp þ1Þ 2: � � RCIi;p ¼ ðICIi þ 1Þ ECIp þ 1 (7) When RCIi;p is larger than 1, that means that industry i is more complex than province p current average complex level, and vice versa.
where industryi;c;t is the output of industry i in province p in time t. Equation (1) shows that an industry i has revealed comparative advan tage (RCA ¼ 1) in province p if the proportion of industry i in the province’s product portfolio is above the average share of the industry to the country’s total. A bipartite network that combines a province and a industry can be presented as a province-industry adjacency matrix Mi;p , where Mi;p ¼ 1 if province p has RCA in industry i and 0 otherwise. Then, the industry relatedness density of a specific industry i in province p in year t is specified as follows. P j RCAj;p;t φi;j;t P Densityi;p;t ¼ (2) j φi;j;t
2.2. Environmental regulation Since 1953, China’s government started to develop very detailed guidelines for country wide social and economic development every 5 years, which is called Five-Year Plan. Before the 10th Five-Year Plan (2001–2005), the previous plans focused on the economic growth with relatively few attention to the environmental issues. Though 10th FiveYear included the concerns of environmental issues into its plan, it did not set a province-level reduction target and implementation is not very effective. Generally, the public and academic regard 11th Five-year plan is a start of official “energy saving and emission reduction” policy. What is more, it is the first time that the central government incorporate the environmental protection as one determinates of the promotion of local leader. To mitigate climate change and reduce the coal use, 11th Five-year plan also required energy consumption per unit of gross domestic product (GDP) reduced by 20% between 2006 and 2010. The china state council issued “Reply to pollution control plan during 11th Five-year plan” in 2006, which handed down the national goal to reduction tar gets in provincial level. Therefore, each provinces and autonomous re gions have set the individual regional emissions targets based on their industrial structure base and development strategy. In the next two Five-year plan (12th Five-year plan (2011–2015) and 13th Five-year plan (2016–2020), the “energy saving and emission reduction” policy continued a significant development strategy, aiming to turn the old heavy industry growth model to a sustainable environ mental friendly and low-carbon economy. In this paper, we use regional emissions reduction targets (ERT) as a proxy for stringency of the emission reduction target. Considering the overlap of the industrial data and climate policy data, we only employ data from the 11th Five-year plan of each province.3 The details of the
φi;j;t is the relatedness between industries i and j, which is defined as: φi;j;t ¼ minfPðRCAi;p;t ¼ 1jRCAj;p;t ¼ 1Þ; PðRCAj;p;t ¼ 1jRCAi;p;t ¼ 1Þg. The relatedness concept is based on the idea that if two industries are more related, they demand similar inputs, including capital, infra structure, and knowledge and so on. A higher value of φi;j;t suggests two industries are more likely to be produced together in the same province. Densityi;p;t is the average RCA of all other existing industries in a given province weighted by their relatedness with this specific industry, which measures the distance between a certain industry and existing industrial structure of a province. An industry with a higher density in a given province indicates that this industry is surrounded by many well developed similar industries. We then apply the method of reflection (Hidalgo and Hausmann, 2009) to calculate the Economic Complexity Index (ECI) of provinces which reflects knowledge accumulated and shown in economic activ ities in provinces, and Industry Complexity Index (ICI) of industries, which measures the knowledge intensity of an industry. By construction, complexity is determined by two factors: 1). the diversity of a province (the number of industries that a province has specialized in (RCA ¼ ¼ 1) and 2). the ubiquity of an industry (the number of provinces that have RCA in a specific industry). Higher diversity means a province are specialized in numerous in dustries, indicating this province is diversified and therefore tends to be more complex in its industrial structures. Low ubiquity demonstrates that if only very few provinces has RCA in a specific industry, then knowledge and capabilities needed to develop this industry is rarely
2 ICI and ECI are standardized index which range from 1 to 1. Considering the existence of negative value, we use ðICIi þ1Þ=ðECIp þ1Þ to reflect the relative complexity level. 3 Data are from the official website National Development and Reform Commission: http://www.ndrc.gov.cn/fzgggz/hjbh/hjjsjyxsh/201106/t2011 0610_417635.html.
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target for each province are shown in Table 1.
is year fixed effects to represent business cycles respectively. In addition, we include some province-specific control variables (Xp;y 1 ): ECI which represents economic complexity of a province; GDP which represents the total GDP level of a province; FDI which indicates Foreign Trade Volume; POP which is the total population. These var iables enable us to capture the variations across provinces in terms of their economic development stage, openness, size and so on.
2.3. Control variables As for control variables, we include GDP per capita, total population (POP), Foreign Trade Volume (FTV). These data are gained from Sta tistical Yearbook of each province. Data used to calculate these variables are collected from the China Province Statistic Yearbooks. These control variables are from the China province Statistic Yearbooks. The descriptive statistics of all variables are shown in Table 24.
3. Empirical results In this section, we start with analyzing the relationship between complexity and coal consumption, to get a general idea about whether more complex industrial structure help energy consumption reduction. Then, we estimate how the emission reduction targets (ERT) affects province’s attractiveness to new industries. In the last robustness check section, we use several variables as proxy for industrial productivity and profitability to investigate the effect of ERT industrial performance.
2.4. Model specification We use a linear probability model with fixed effects to predict the entry probability of specific industries in Chinese province. The speci fication of the entry model is as follows: Entryi;p;y ¼ α þ β1 *Density i;p;y þ β4 *ERTP *Density i;p;y
1
1
þ β2 *RCIi;p;y
1
þ β3 *ERTP
þ β5 *ERTP *RCIi;p;y
1
þ β*Xp;y
1
3.1. Complexity and coal consumption
þ δip þ λy (8)
Before proceeding to the regression results, we first describe the re lationships between complexity and the coal consumption, in both in dustrial and province level. Fig. 1 displays the scatter plot of complexity and the ratio of coal consumption to the output added value (Coal consumption) at 2-digit industrial level.5 Obviously, there is a negative relationship between coal consumption per added value and complexity, indicating more complex industries indeed consume less coal when producing an addi tional value of output. This is similar to what Sun et al (2019a) find in their study, which indicates emerging service sectors improve CO2 emission efficiency, but the traditional sectors hinder the efficiency improvement. To some extent, this preliminary results suggest devel oping more complex industry is a way to reduce the coal consumption. Fig. 2 exhibits the scatter plot of knowledge complexity and the ratio of coal consumption to the regional GDP at province level. We can clearly see that coal consumption per GDP and complexity is negatively correlated. Such a result suggest having more complex knowledge embedded in industrial structure is more eco-friendly and reduces the coal consumption to per unit of GDP, which further suggests developing more complex industry probably can reduce the coal consumption at province level and contributes to the climate mitigation. Table 3 shows how the improvement of knowledge complexity of a province contributes to the coal consumption. Following Shao et al. (2019a), we use the ratio coal consumption to GDP, coal consumption per capital and the total coal consumption to demonstrate the coal consumption level of a province. No matter in which coal consumption definition, our results show the growth rate of complexity (KCI Growth Rate) decreases the coal consumption to GDP, coal consumption per capital and the total coal consumption. Overall, the result confirms that more complex industrial structure contributes to the coal consumption reduction.
where Entryi;p;y equals 1 only when province p does not have RCA in industry i for years before y and gains RCA for after years y, which is used to demonstrate whether an industry is an newly developed industry to a province. This two-year smoothing is used to avoid RCA fluctuation around the threshold. Density i;p;y 1 is industrial relatedness density, measureing the distance between industry i and the other existing in dustries in province p at year y 1. RCIi;p;y 1 represents the difference in industry complexity of industry i and economic complexity of province p at year y 1. ERTp is provincial emission reduction targets in the 11th five-year plan. Considering the potentially heterogeneous effects of emission reduction targets on different industries, we also include the cross term of ERTp and RCIi;p;y 1 (ERTp *RCIi;p;y 1 ) to examine the whether province with stricter environmental regulation are more likely to gain more complex industries, and the cross term of ERTp and Densityi;p;y 1 Table 1 Provincial energy intensity reduction targets for the 11th FYP period. Province
Target (%)
Province
Target (%)
Province
Target (%)
Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang
20 20 20 22 22
Anhui Fujian Jiangxi Shandong Henan
20 16 20 22 20
Sichuan Guizhou Yunnan Tibet Shan’xi
20 20 17 12 20
20 20 20 20 20 20
Hubei Hunan Guangdong Guangxi Hainan Chongqing
20 20 16 15 12 20
Gansu Qinghai Ningxia Xinjiang
20 17 20 –
3.2. The effect of environmental regulation on the industrial diversification
(ERTp *Densityi;p;y 1 ) to test how climate policy affects the pathdependent process. δip represents province-industry fixed effect to con trol the unobserved factors changes across industry-province pair, and λy
Table 4 shows the main result regarding the relationship between emission target, complexity and industrial diversification. Column (1) shows the baseline regression without considering the effect of emission targets. Specifically, we can see that the coefficient of relatedness density (Density) is significantly positive, indicating density increase the probability of branching into new industries in a province.
4
The period selection period is based on the data availability. The industrial data of each province is from 1999 to 2011. Therefore, the province-level control variables used in the analysis are also from 1999 to 2011. Consid ering the overlap of the industrial data and climate policy data, we only employ data from the 11th Five-year plan of each province. Therefore, the period of climate policy data is from 2006 to 2010. In our empirical analysis, when the climate policy is considered, the analyzed data is from 2006 to 2011(climate policy is lagged by one period).
5 Since we can only get the industrial coal consumption data at 2-digit level, here we report the scatter plot at 2-digit level. In the following industrial diversification level analysis, we conduct the empirical analysis at 3-digit level.
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Table 2 Descriptive statistics of variables. Variable
Definitions
Obs
Unit
Level
Mean
Std. Dev.
Min
Max
RCA DENSITY RCI No. Firms
RCA Related Density Ratio of Complexity The number of firms in an industry The number of un-profitable firms in an industry The output of an industry Add value of output of an industry
54,325 54,325 54,325 54,340
– – – 1 unit
Province_ industry Pair
0.35 28.66 1.04 63.30
0.48 13.33 0.10 164.60
0.00 0.57 0.79 0.00
1.00 100.00 1.35 8994.00
54,338
1 unit
10.05
23.08
0.00
762.00
54,338 45,357
10,000 Yuan 10,000 Yuan
7203134.00 903724.10
25100000.00 3241608.00
0.00 6079106.00
842000000.00 92200000.00
ICI Coal_Indsutry
Industry complexity index Coal consumption of an industry
2087 434
– 10,000 Tons
0.01 6118.45
0.02 19756.02
0.04 1.39
0.06 170744.20
GDP
Gross Domestic Product
403
7702.23
8322.26
105.61
53210.28
FTV POP
Foreign Trade Volume Total population (10,000 person) Economic complexity index Total Coal Consumption Total Coal Consumption/GDP
403 403
100,000,000 Yuan 1000 US Dollars 10,000 person
2829465.00 4185.93
7354474.00 2642.18
39.84 256.00
55000000.00 10504.85
0.03 8757.80 1.59
0.10 7300.22 1.33
0.21 179.00 .146
0.24 38921.00 9.13
Total Coal Consumption/Total population
386
2.28
1.86
.23
13.98
No.Unprofitable Output Added Value
ECI Coal_Province Coal Consumption per GDP Coal consumption per capital
403 397 386
Industry(3 digit) Industry(2 digit)
Province
– 10,000 Tons Tons/10,000 Yuan Tons/person
Fig. 1. Industrial Complexity and Coal Consumption/Output Added Value (2 digit).
This result is consistent with the previous relatedness and industrial diversification literature, which argues industries more related to the existing industries of a region are more likely to be well developed in that region (Boschma and Capone, 2015; Balland et al., 2019; Guo and He, 2017; Zhu et al., 2017). Column (2) shows the overall effects of ERT policy. We can see that emission reduction target plays a significant negative role in the entry probability of new industries after considering the fixed effects. In the other words, stricter regulation discourage new industries to settle in one province. This is one of the negative impacts brought by the mission reduction target since it is harmful to developing diversified industries. Column (3) to Column (5) shows the heterogeneous effects of in dustrial land subsidy on industries with different levels of related den sity and complexity. In Column (3), we find that interaction term
ERT�RD is significantly positive. Stricter reduction targets reduces the probability of branching into new industries, but this negative effect is weakened for industries that is closely related to the current industrial structure of a province. This is not a good news for regions since such a result demonstrates that ERT policy will strengthen the path-dependent process and is not beneficial for industrial diversification. In Column (4), ERT�RCI has a significantly positive effect on entry probability. For industries with higher complexity, the negative effect of ERT on entry probability is weaker. In the full model (Column (5)) with both the cross term of ERT�RD and ERT�RCI, our results are still robust. When RCI is larger than 1.8, ERT policy environmental regulation’s effect turns to positive. However, from Table 2, we can see that the maximum value of RCI is 1.35. Therefore, this threshold is only a theoretical value. Taking together, stricter environmental regulation is not helpful in 5
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Journal of Environmental Management 260 (2020) 110148
Fig. 2. Knowledge complexity and coal consumption/GDP
3.3. Robustness check
Table 3 Province-level analysis: The effect of complexity on province coal consumption. Variables
KCI Growth Rate Log(GDP) Log(FTV) Log(Population) Coal Consumption/ GDP Coal Consumption per capita Log(Coal Consumption) Constant Observations Year FE Province FE Adjusted Rsquared Within R-squared
(1)
(2)
(3)
Coal Consumption per GDP
Coal Consumption per capita
Log(Coal Consumption)
0.00141*** (0.00009) 0.03944 (0.10743) 0.05460 (0.03376) 0.34117* (0.19081) 0.88167*** (0.04140)
0.00197*** (0.00023) 0.28761 (0.18376) 0.03456 (0.03970) 0.13943 (0.33174)
0.00104*** (0.00020) 0.03891 (0.07613) 0.00324 (0.01512) 0.27509 (0.16569)
1.12269*** (0.06838) 3.59977** (1.37278) 344 YES YES 0.90899
0.84590 (2.05959) 344 YES YES 0.95433
0.84571*** (0.06291) 3.20426** (1.34441) 343 YES YES 0.96450
0.91323
0.95646
0.96616
To further test the effect of environmental regulation on industrial performance, we use other dependent variables a robustness check. We first use the total industrial output and added value of industrial output to test the effect of ERT on industrial productivity. Then, we employ the number of the firms that does not make profits in each province, and its ratio to the total number of firms in one industries as proxies for in dustrial profitability. The results of productivity and profitability are shown in Table 5 and Table 6 respectively. Column (1) and Column (3) in Table 5 shows the overall effects of ERT on the total output and added value of industrial output. When not considering the heterogeneity in industries, the coefficient of environ mental regulation is significantly negative in both models, which means environmental regulation lowers the productivity of an industry. In Column (2) and Column (4), the significantly positive coefficient of ERT*RCI suggests negative effect of ERT on industrial performance is weaker for more complex industries. Specifically, if RCI is larger than 1.48, ERT can improve the output rather than exerting a negative effect. This figure is 1.47 for added value of industrial output. Table 6 shows the effects of ET on profitability of industries. The overall effect of ET is positive (Column (1) and (3)). That is to say, ET will increase the number of firms that do not make profit in an industry and also the non-profit firm’s ratio in a given industry. The coefficient of ET*RCI is significantly negative(Column(2) and (4)), indicating if an industry is more complex, this industry will less negatively affected by regulation and won’t have so many unprofitable firms. When RCI is larger than 1.44, regulation reduce the number of unprofitable firms on the contrary. To sum up, results shown in Tables 5 and 6 further confirm the finding in previous part: ET have a negative effect overall but is less harmful to industries with higher complexity.
Note: All independent variables are mean-centered and lagged by one period; Robust standard errors are in parentheses and are clustered at the province level; ***p < 0.01, **p < 0.05, *p < 0.1.
developing new industries, especially for industries that is less related to the existing specialized industries. However, for industries with higher level of complexity, this effect of ERT is less harmful. Theoretically, for industry with extremely high complexity, the emission reduction targets exert a positive effect rather than a negative one. These results are generally in line with the environmental regulation literature suggesting regulation crowd out the low-end industries and helps the industrial upgrading in the long run.
4. Conclusion China has witnessed a rapid industrial development the last several decades. The progress in manufacturing industries makes an enormous contribution to the economy, but at the same time leads to some climate concerns due to excessive use of coal by heavy industries. With the 6
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Journal of Environmental Management 260 (2020) 110148
Table 4 The effect of environmental regulation. Variables
(1)
(2)
(3)
(4)
(5)
0.00224*** (0.00073) 0.01957 (0.07349) 0.00833*** (0.00250)
0.00165** (0.00075) 0.08706 (0.07951) 0.00941*** (0.00247) 0.00005** (0.00002)
0.00196*** (0.00074) 0.17217* (0.09903) 0.01622*** (0.00406)
0.00038 (0.00086) 0.33375*** (0.10201) 0.03465*** (0.00526) 0.00015*** (0.00003) 0.01925*** (0.00323) 0.02825 (0.04461) 0.01135 (0.00768) 0.09972 (0.06833) 0.35629 (0.73128) 16,710 YES YES 0.01814 0.01890
Entry Density RCI ERT
0.00213*** (0.00071) 0.00947 (0.06837)
ERT*DENSITY ERT*RCI Log(GDP) Log(FTV) Log(Population) Constant Observations Year FE Province-Industry FE Adjusted R-squared Within R-squared
0.01279 (0.04360) 0.01278* (0.00762) 0.03500 (0.06808) 0.37047 (0.72852) 17,109 YES YES 0.01416 0.01473
0.00358 (0.04463) 0.01331* (0.00767) 0.02658 (0.06837) 0.48230 (0.73056) 16,710 YES YES 0.01513 0.01577
0.00660*** (0.00223) 0.01834 (0.04484) 0.01344* (0.00768) 0.05176 (0.06816) 0.29685 (0.73420) 16,710 YES YES 0.01562 0.01633
0.01410 (0.04485) 0.01255 (0.00767) 0.02648 (0.06834) 0.61823 (0.73428) 16,710 YES YES 0.01556 0.01627
Note: All independent variables are mean-centered and lagged by one period; Robust standard errors are in parentheses and are clustered at the province and industry level; ***p < 0.01, **p < 0.05, *p < 0.1. Table 5 The effect of ERT on productivity. Variables
(1)
(2)
(3)
Log(Output) Log(Output) RCI ERT ERT*RCI KCI Log(GDP) Log(FTV) Log(Population) Log(Added Value) Constant Observations Year FE Province-Industry FE Adjusted R-squared Within R-squared
0.34695*** (0.01076) 1.99289 (1.40786) 0.11777*** (0.02647)
(4)
Log(Added Value)
5.47554*** (1.59934) 2.06566*** (0.28205) 0.01631 (0.04146) 1.76479*** (0.24028)
0.33183*** (0.01092) 8.49925*** (1.45511) 0.55756*** (0.04208) 0.37551*** (0.02694) 1.01755 (1.63597) 0.74887*** (0.26850) 0.01094 (0.04191) 0.27968 (0.24483)
7.16694* (4.09773) 26,023 YES YES 0.41234 0.41261
13.19406*** (4.03360) 26,023 YES YES 0.43207 0.43235
14.62116*** (2.89344) 0.15561*** (0.02655) 32.15850*** (3.39743) 2.50830*** (0.61585) 0.11228 (0.09669) 4.37557*** (1.18662) 0.49779*** (0.01090) 35.68830*** (12.16868) 17,308 YES YES 0.94160 0.94164
19.48646*** (2.79538) 0.73371*** (0.04198) 0.49901*** (0.02777) 18.89839*** (3.33617) 0.76403 (0.58427) 0.14924 (0.09416) 6.48160*** (1.20686) 0.47923*** (0.01087) 18.90067 (12.05460) 17,308 YES YES 0.94436 0.94440
Note: All independent variables are mean-centered and lagged by one period; Robust standard errors are in parentheses and are clustered at the province and industry level; ***p < 0.01, **p < 0.05, *p < 0.1.
increasing demand for quality of life and importance of sustainable development, government has applied an array of mitigating measures to reduce CO2 emission and cope with the climate change, one of which is “energy saving and emission reduction” policy since its 11th Five-year plan. This paper attempts to estimate impact of this “energy saving and emission reduction” policy from industry complexity perspective. Our result shows: Overall, more complex industrial structure contributes to the coal consumption reduction, which suggests developing more complex
industry is a way to reduce the coal consumption and mitigate the climate change. In general, emission reduction target policy reduces the probability of branching into new industries. What is more, emission reduction target policy has a negative effect on the productivity and profitability of an industry as well. Compared with lower complex in dustry, the higher complex industry is less harmed. Theoretically, for industry with extremely high complexity, the emission reduction targets exert a positive effect rather than a negative one. Our contribution to the literature is threefold. First, we move beyond 7
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Journal of Environmental Management 260 (2020) 110148
policies, such as subsidies for renewable energy, environmental resource tax, emissions trading system and so on, is necessary for designing a more effective strategy to achieve a better industrial performance and alleviate environmental issues at the same time.
Table 6 The effect of ET on industrial profitability. Variables
(1)
(2)
Log(No.Unprofitable Firms) Log(No. Unprofitable Firms) RCI ERT ERT*RCI KCI Log(GDP) Log(FTV) Log (Population) Unprofitable Ratio Constant Observations Year FE ProvinceIndustry FE Adjusted Rsquared Within Rsquared
0.14638*** (0.00787)
0.14100*** (0.00783)
0.53169 (0.51434) 0.01248*** (0.00343) 0.69011 (0.60231) 0.11651 (0.08116) 0.06495*** (0.01256) 0.77538*** (0.11928)
1.27536** (0.51882) 0.06432*** (0.00578) 0.04444*** (0.00390) 0.11548 (0.60370) 0.28203*** (0.08283) 0.06564*** (0.01250) 0.95995*** (0.12028)
7.06438*** (1.49202) 26,023 YES YES
(3)
(4)
Unprofitable Ratio
Author contributions 0.38645** (0.15457) 0.00656*** (0.00130)
Dong ZYZ and Wang SJ designed the research; Dong ZYZ performed experiments and computational analysis; Dong ZYZ drafted the paper; Dong ZYZ and Chen WQ contributed to the interpretation and the preparation of the manuscript, and Dong ZYZ and Wang SJ contributed to the final draft of the manuscript.
6.39777*** (1.50600) 26,023 YES YES
0.62673*** (0.16346) 0.12219*** (0.02395) 0.01404*** (0.00419) 0.05648* (0.03154) 0.10202*** (0.01311) 0.57518 (0.43620) 22,836 YES YES
0.53257*** (0.15636) 0.01678*** (0.00215) 0.00858*** (0.00121) 0.51690*** (0.16365) 0.09023*** (0.02425) 0.01301*** (0.00422) 0.02215 (0.03137) 0.09840*** (0.01311) 0.41756 (0.43918) 22,836 YES YES
0.20286
0.20715
0.13091
0.13396
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0.20323
0.20755
0.13137
0.13445
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Declaration of competing interest None. Acknowledgments This research was funded by the National Natural Science Founda tion of China (Grant Nos. 71473222, 71874156), the Fundamental Research Funds for the Central Universities (19lgzd09), Guangdong Special Support Program snf Pearl River S&T Nova Program of Guangzhou (201806010187).
Note: All independent variables are mean-centered and lagged by one period; Robust standard errors are in parentheses and are clustered at the province and industry level; ***p < 0.01, **p < 0.05, *p < 0.1.
the previous literature on environmental regulation on economic output and industrial development from a macro-level perspective and inves tigate the effect of emission reduction policy from relative micro in dustrial perspective. Second, focusing on economic complexity of industries extends the industrial diversification literature which mainly pays attention to industrial relatedness Third, we aim to contribute to the industrial diversification literature by analyzing whether the regional industrial branching process can be influenced by uncertainty induced by policy interventions. Our finding indicates this “emission reduction” policy is harmful in attracting new industries to settle in one region and has a negative effect on the productivity of an industry, which supports the pollution heaven hypothesis that asserts firms prefer to locate in places with lower envi ronmental standards to reduce higher environmental compliance costs. As more complex industrial structure contribute to the coal consumption reduction and economic growth as well, it is extremely of importance to attract and develop more complex industries in one region. Considering the emission reduction targets currently has negative effect on all in dustries (though more complex industry is less harmed), it is also necessary to provide subsidies though other policies, such as specific tax preference policy for specific industries, to offset negative effect of the regulation and help region to attract these industries to settle down. However, due to the data limitation, our results is a short-run anal ysis. Therefore, whether this emission reduction policy can have positive effect on industry structure upgrade in the long term is still an open question and need further analysis. It will be a promising future research direction to evaluate both the benefit and the cost of such a policy on climate and industrial development in a long run. In addition, we only estimate the effect of emission reduction policy on industrial perfor mance. However, with the increasing importance role of environment in the society development, various command and control policy and market-based policy has been adopted by Chinese government. There fore, the comparison of efficiency of different kinds of environmental 8
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