How does China's Western Development Strategy affect regional green economic efficiency?

Journal Pre-proof How does China's Western Development Strategy affect regional green economic efficiency?

Chengfeng Zhuo, Feng Deng PII:

S0048-9697(19)35934-0

DOI:

https://doi.org/10.1016/j.scitotenv.2019.135939

Reference:

STOTEN 135939

To appear in:

Science of the Total Environment

Received date:

21 October 2019

Revised date:

2 December 2019

Accepted date:

3 December 2019

Please cite this article as: C. Zhuo and F. Deng, How does China's Western Development Strategy affect regional green economic efficiency?, Science of the Total Environment (2018), https://doi.org/10.1016/j.scitotenv.2019.135939

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© 2018 Published by Elsevier.

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How does China’s Western Development Strategy Affect Regional Green Economic Efficiency?

Chengfeng Zhuo ab， Feng Deng ab* a

Center of Innovation Management in Xinjiang, Xinjiang University School of Economics and Management, Xinjiang University

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Chengfeng Zhuo: Male, doctoral candidate of school of economics and management, Xinjiang University, majoring in western economics. Corresponding address is NO. 666 Shengli Road, Tianshan District, Urumqi City, Xinjiang Uygur Autonomous Region. E-Mail: [email protected].

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Feng Deng: Corresponding author, male, professor, doctoral supervisor. Corresponding address is NO. 666 Shengli Road, Tianshan District, Urumqi City, Xinjiang Uygur Autonomous Region. E-Mail: [email protected].

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How does China’s Western Development Strategy Affect Regional Green Economic Efficiency?

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Abstract: Narrowing regional economic gaps and constructing an environmentally friendly society are two major objectives of China’s current economic policies. Promoting green development in resource-based regions is a global issue. Focusing on China’s Western Development Strategy (WDS), this study first calculates the provincial green economic efficiency (GEE) in China. The synthetic control method is adopted to evaluate the net effect of WDS on regional GEE. The transmission mechanisms are then investigated in perspective of the interregional flow of innovation factors. The results show that: (1) The GEE in coastal areas of China is generally higher than that of western China; (2) The WDS can improve the overall regional GEE but the effect decays over time and through the diversity of the regions; (3) WDS can improve regional GEE by introducing innovation factors into the western regions, further improving the regional industrial structure, urbanization, and labor quality; (4) The optimal scale of innovation factors flowing into the WDS regions is calculated. The transmission mechanisms will have a positive effect on the GEE of the western regions simultaneously only if the inflow scale of the innovation factors varies on the interval (0.347, 0.618). The paper concludes with targeted policies to promote regional green development.

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Keywords：Green economic efficiency; Western Development Strategy; Synthetic Control

1. Introduction

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Method; Panel Smoothing Transformation Regression; Transmission mechanisms.

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The Kuznets curve reflects the substitution relationship between economic development and environmental pollution (Grossman and Krueger, 1995). Sacrificing the environment and resources for economic development is common among developing countries (Zhu et al., 2019; Wang et al., 2018). However, at present, the world is facing pressures from an economic downturn and environmental pollution. Environmental degradation causes 12.6 million deaths every year. Globally, one in four deaths is caused by environmental problems 1. Thus, promoting green development and finding the balance between economic development and environmental protection is of great importance to governments and academia (Wang and Shao, 2019; Amigues and Moreaux, 2019; Li et al., 2017; Carfí et al., 2019). China’s economy has grown rapidly and accumulated rich experience since the economic reforms launched in 1978 (Yang et al., 2013). However, China also faces significant resource and environmental constraints. In 2016, 75.1% of all Chinese cities had high levels of air pollution. China’s carbon emissions totaled 10 billion tons in 2018, an increase of 2.3% from 2017. Its economy urgently needs to shift to a green economic development mode. In terms of resource endowments, China’s western regions contain rich natural resources and energy reserves that can provide energy security for the country’s economy. However, China’s western regions are also 1

On December 4, 2017, Eric Solheim, executive director of the United Nations Environment Programme, released the latest statistics of global environmental pollution status and loss data in 2017 at the Third United Nations Environment Conference in Nairobi, Kenya.

Journal Pre-proof underdeveloped economically and the gap between the eastern and western regions shows signs of further expansion. In an attempt to rectify this economic imbalance, China implemented the Western Development Strategy (WDS) in 2000. The WDS has led to the relocation of traditional industries from the east to the west (Zhang et al., 2019). In recent years, the WDS has attached increasing importance to the efficiency of energy extraction and the environmental damage extraction can cause, accelerating the process of green development. To illustrate the changing trend of China’s regional green economic efficiency (GEE)2, the GEE difference between China’s western regions, other regions, and the country as a whole is calculated. 0.300 0.250

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Difference of GEE between China's western regions and the whole country Difference of GEE between China's and the non-western regions

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Fig. 1.1 GEE difference between China as a whole and non-western regions As shown in Fig. 1.1, the differences in GEE between China’s western regions, non-western regions, and the country as a whole have had synchronous evolution. Before the WDS implementation, the difference in GEE between the western regions and the country as a whole was more than 0.15. The difference rose to 0.24 when compared to all other regions. However, after implementation of the WDS, the differences show a wavy downward trend, indicating that the WDS may be conducive to improving the GEE of the western regions. The WDS has provided China with practical experience in promoting green development in resource-based regions. However, the effect of the WDS on the environment is still uncertain (Yin et al., 2017; Zhang et al., 2019; Yang et al., 2018). If the WDS is conducive to finding the balance between resource exploitation, environmental protection, and economic development, what factors have led to this positive impact? More importantly, can a “feasible plan” to achieve the dual goals of economic development and environmental protection be extracted from China’s regional development strategy? To answer these questions, we use the WDS to investigate the promotion of green development through scientific regional policies. The results may be of great significance for other developing countries to achieve green development. The contributions of this study are as follows: (1) The net effect of the WDS on regional GEE is estimated reliably using the synthetic control method (SCM) that can avoid sample selection bias and policy endogeneity; (2) In terms of the interregional flow of innovation, the transmission processes of the WDS to regional GEE are investigated; (3) A panel smooth transition regression (PSTR) model is used to calculate the optimal scale of innovation factors flowing into the regions covered by the WDS. On this optimal 2

The specific calculation process will be detailed in 3.2 variable measurement.

Journal Pre-proof scale, the WDS can promote regional green development through three transmission processes simultaneously; (4) This study indicates that identifying the innovation factors scientifically is vital in developing a green economy, especially for resource-based countries or regions; (5) The findings and suggestions can provide insights for other developing countries, especially resource-based regions, to achieve green development. The remainder of the study is structured as follows: Section 2 reviews the existing research; Section 3 introduces the methodology and data; Section 4 presents the results of the empirical analysis; Section 5 offers discussions; finally, Section 6 contains conclusions and recommendations.

2. Literature review

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As environmental constraints and economic pressures mount, an increasing number of scholars have focused on the development of a green economy. The research is mainly carried out from two perspectives. One focuses on the measurement and comparative analysis of the level of green development (Chung et al., 1997; Kumar, 2006; Tao et al., 2016; Mousavi-Avval et al., 2011; Wang et al., 2013; Luukkanen et al., 2019) and provides the background for the GEE calculation in this study. The other explores the factors that influence green development, mainly focusing on the impact of economic activities such as financial expenditures (Lin and Zhu, 2019), environmental regulations (Li and Wu, 2017), outward foreign direct investments (Hao et al., 2020), technological innovations (Song et al., 2019), and structural and efficiency transformations (Mao et al., 2019). Recently, attention has focused on the specific policies that may be the influencing factors in green development. Wu et al. (2019) investigated the impact of the “Policy of Electricity Substitution” on energy savings and emissions reductions and found that the policy alone is not sufficient to reduce dependence on fossil-fuel energy under the current power generation structure. Liu and Xin (2019) used a Global Malmquist Luenberger (GML) index based on a Slacks-Based Measure (SBM) directional distance function and regression discontinuity to evaluate China’s provincial green total factor productivity. They found that the Belt and Road Initiative has significantly promoted green factor productivity along the belt and road. Despite the fact that the WDS has the longest history and the most influence among all of China’s regional development strategies, its impact on regional green development and the underlying mechanisms have not been sufficiently researched. In terms of the WDS, which has been in existence for nearly 20 years, scholars have done abundant research on its influence on economic development. Relevant research is mainly carried out in two directions, one focusing on evaluating its economic performance and the other on examining its impact on environmental issues such as energy consumption and pollution. Relevant studies on the economic performance of the WDS have not reached a consensus. Scholars who hold a positive view have indicated that the WDS has promoted the economic growth of the western regions and spurred rapid growth over its first years. For example, Liu et al. (2009) established a growth model and found that the WDS increased the annual growth rate of the western regions by about 1.5% since 2000 and that those regions had started to catch up with or even surpass some eastern regions. In addition, the comprehensive economic strength of the western regions has been significantly enhanced since China’s State Council issued its “Several Opinions on Deeply Implementing the Western Development Strategy” in 2010. As well, since the

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WDS can reduce the effective corporate income tax rate by 11.5%, it has promoted an increase in investments in human capital, the labor force, and fixed assets, ultimately improving the productivity of existing enterprises (Wu et al., 2017). The WDS also promotes the rationalization and upgrading of western regions’ industrial structure, especially in cities with high administrative levels (Yuan and Zhu, 2018). However, scholars with a negative view have argued that although it can improve the western regions’ economic indicators, the economic imbalance between the eastern and western regions has not been effectively narrowed. For example, Peng and Chen (2016) estimated the WDS effect through the systematic Generalized Method of Moments (GMM). They found that implementing the WDS has not significantly improved the Gross Domestic Product (GDP) growth rate and that a comprehensive development level has not converged. Yue and Bai (2008) found that both the absolute economic gap and the relative economic gap became larger. Liu and Zhao (2015) argued that “policy traps” existed in the implementation of the WDS. They also found that local governments were overly focused on fixed-asset investments and energy exploitation but ignored institutional reforms and soft environmental construction. They claimed that this has prevented the WDS from effectively promoting China’s economic growth. Others have found that China’s regional development gaps will continue to expand despite the WDS (Fleisher et al., 2010; Cheong and Wu, 2014). As well, Luo et al. (2019) found that expanding the tax base would not offset the impact of the lower corporate tax rate under the WDS, ultimately leading to a significant reduction in revenues. Thus, the economic performance of the WDS is still uncertain and requires further investigation. Research focusing on the environmental impact of the WDS has also not reached a consensus. Scholars who hold a negative view state that carbon emissions are closely related to economic development (Tan et al., 2016; Li et al., 2019) and that this relationship is particularly obvious in the western regions (Zhang, 2011). Large-scale infrastructure construction, resource extraction, and mining resulted in a significant increase in carbon dioxide emissions during the implementation of the WDS (Chen et al., 2010; Huang et al., 2011; Wang et al., 2016). Moreover, Zhang et al. (2019) used a PSM-DID method to evaluate the WDS impact on carbon dioxide emissions. They found that although the WDS had varied impacts on carbon intensity in different regions, it did not reduce carbon intensity of the western regions as a whole and that carbon intensity continues to grow rapidly. In contrast, other studies hold a positive view of the environmental impact of the WDS. Yang et al. (2018) constructed an ecological productivity index and found that western China’s ecological productivity score has been rising continuously since WDS implementation. Zhang et al. (2017) reached a similar conclusion, arguing that although the WDS has promoted interregional industrial transfers, it has not induced the “pollution refuge effect”. However, three issues require further research. Firstly, existing literature has not yet evaluated the WDS performance on simultaneous economic development and environmental protection. If only a “single objective function” is used to evaluate the WDS performance (economic growth or environmental protection), the conclusions will not be applicable to China’s current economic situation. Secondly, extant research on the performance of the WDS is either vague (Yang et al., 2018) or controversial (Yin et al., 2017; Zhang et al., 2017), hindering further implementation of the WDS. Thirdly, there is no in-depth research on how the WDS affects regional green development, hindering effective green development in resource-based regions.

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3. Methodology and data 3.1 Estimation method 3.1.1 Synthetic Control Method

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In January 2000, China’s State Council issued the guidelines for WDS implementation after holding a conference on the development of the western region. As shown in Fig. 3.1, the WDS has been implemented in 12 provinces, autonomous regions, and municipalities, namely Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, Inner Mongolia, and Guangxi.

Fig. 3.1 Regions covered by China’s WDS This study uses the SCM to evaluate the WDS impact on the GEE of these jurisdictions. Refining the “net effect” is essential to accurately evaluate policy performance. Abadie and Gardeazabal (2003) proposed the SCM to study the cost of terrorist activities in the Basque region. SCM is now widely used to evaluate the performance of policies such as tobacco restrictions (Abadie et al., 2010), trade liberalization (Olper et al., 2018), and energy economy (Kim and Kim, 2016). The advantages of SCM are that it chooses the optimal weight of the linear combination, thus reducing the simple selection bias and avoiding policy endogeneity, and it provides a synthetic control object for each individual, thus avoiding individual differences. Assume that the economic output Yjt of J+1 regions in period T can be observed. The first region belongs to the target area of the WDS (treatment group). The remaining J regions are not affected by the WDS (control group). T0 represents the periods before WDS implementation. 𝑌0𝑗𝑡 is the GEE of the control group. 𝑌1𝑗𝑡 is the GEE of the treatment group. When t∈ [1, 𝑇0 ], 𝑌0𝑗𝑡 =𝑌1𝑗𝑡 .

Journal Pre-proof When t∈ [𝑇0 , 𝑇], 𝑌0𝑗𝑡 =𝑌1𝑗𝑡 -𝛽𝑗𝑡 . 𝛽𝑗𝑡 represents the increment of GEE brought to region j at period t by the WDS. If 𝛽𝑗𝑡 > 0, it indicates that the WDS can improve the regional GEE. If 𝛽𝑗𝑡 ＜0, it indicates that the WDS can block the regional GEE. However, the GEE 𝑌1𝑗𝑡 can be observed directly but the GEE 𝑌0𝑗𝑡 , which is not affected by WDS after T0, cannot be obtained. Therefore, 𝛽𝑗𝑡 is determined by the baseline model proposed by Abadie et al. (2010):

𝑌𝑗𝑡0 = 𝛼𝑡 + 𝛿𝑡 𝑍𝑗 + 𝜃𝑡 𝜇𝑗 + 𝜀𝑗𝑡

(1)

where 𝛼𝑡 represents the time-fixed effect that affects the GEE; 𝑍𝑗 is a vector with (r, 1)

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dimension that represents the observable variables that are not affected by the WDS; 𝛿𝑡 is the parameter of the control variables; 𝜇𝑗 represents the fixed effect that cannot be observed in a

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specific region; 𝜃𝑡 represents the time effect that cannot be observed; and 𝜀𝑗𝑡 is the unobservable

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instantaneous shock that cannot be observed in each region. According to the principle of SCM, a weight vector 𝑊 = (𝑤2 , ⋯ , 𝑤𝐽+1 )′ is formed for the

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regions affected by the WDS. For any j, 𝑤𝑗 ≥ 0 and 𝑤2 + ⋯ 𝑤𝐽+1 = 1. Vector W represents the potential synthetic control combination of the treatment group. Each 𝑤𝑗 measures the synthetic contribution rate of the control group to the treatment group. Then the resultant variable of the synthetic control is equal to: 𝐽+1

𝐽+1

𝐽+1

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∑ 𝑤𝑗 𝑌𝑗𝑡 = 𝛼𝑡 +𝛿𝑡 ∑ 𝑤𝑗 𝑍𝑗 + 𝜃𝑡 ∑ 𝑤𝑗 𝜇𝑗 + ∑ 𝑤𝑗 𝜀𝑗𝑡 𝑗=2

𝑗=2

𝑗=2

(2)

𝑗=2

𝐽+1

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∗ Suppose that for the treatment group region j=1, there exists vectors (𝑤2∗ , ⋯ , 𝑤𝐽+1 ):

𝐽+1

∑ 𝑤𝑗∗ 𝑌𝑗1 𝑗=2

=

𝑌11 , ∑ 𝑤𝑗∗ 𝑌𝑗2 𝑗=2

𝐽+1

=

𝑌12 , ⋯ , ∑ 𝑤𝑗∗ 𝑌𝑗𝑇0 𝑗=2

𝐽+1

= 𝑌1𝑇0 , ∑ 𝑤𝑗∗ 𝑍𝑗 = 𝑍1

(3)

𝑗=2

𝑇

0 If ∑𝑡=1, 𝜆′𝑡 𝜆𝑡 is non-singular, the following formula is established:

𝐽+1

𝐽+1

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𝑇0

𝑌𝑗𝑡0 − ∑ 𝑤𝑗∗ 𝑌𝑗𝑡 = ∑ 𝑤𝑗∗ ∑ 𝜆𝑡 (∑ 𝜆′𝑡 𝜆𝑡 ) 𝑗=2

𝑗=2

𝑠=1

𝑡=1

−1

𝐽+1

𝜆′𝑠 (𝜀𝑗𝑠 − 𝜀1𝑠 ) − ∑ 𝑤𝑗∗ (𝜀𝑗𝑠 − 𝜀1𝑠 )

(4)

𝑗=2

Because the left side of formula (3) approaches 0 (Abadie et al., 2010), 𝑌0𝑗𝑡 can be 𝐽+1

substituted by ∑𝑗=2 𝑤∗𝑗 𝑌𝑗𝑡 approximately. Then, the unbiased estimator of 𝛽𝑗𝑡 can be expressed as:

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𝐽+1

𝛽̂ 1𝑡 =

𝑌𝑗𝑡0

− ∑ 𝑤𝑗∗ 𝑌𝑗 , t ∈ (𝑇0 , 𝑇]

(5)

𝑗=2

The key to estimating 𝛽̂ is 𝑊∗ = (𝑤∗2 , ⋯ , 𝑤∗𝐽+1 )′. Abadie et al. (2010) determined the 1𝑡 optimal weight W* by minimizing the distance between X1 and X0:

min ‖𝑋1 − 𝑋0 𝑊‖𝑣 = √(𝑋1 − 𝑋0 𝑊)′ 𝑉(𝑋1 − 𝑋0 𝑊)

(6)

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where X1 is the (k, 1) dimension eigenvector Z, which belongs to region j=1 before WDS implementation. The jth column of X0 is assumed to be the eigenvector Z of region j (j>1) before WDS implementation. Z contains the main factors that affect the GEE, which are called the prediction variables. V is a symmetric positive semidefinite matrix with (k, k) dimension. We look for V*, which satisfies the Mean Square Percentage Error (MSPE) minimization condition, to determine 𝑤∗𝑗 . This 𝑤𝑗∗ can make the synthetic GEE of the treatment group before the WDS change with its real GEE. At the same time, in order to avoid the estimation bias caused by the gap between the treatment group and the control group, the combination of predictive variables is restricted to the convex set of predictive variables in the control group (King and Zeng, 2006), that is, 𝑤𝑗∗ ≤ 0. Finally, we use the SCM proposed by Abadie and Gardeazabal (2003) to evaluate the impact of the WDS on regional GEE.

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3.1.2 Difference-in-difference model

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To further test the reliability of the baseline analysis, we choose the difference-in-difference (DID) method, another common causal inference method, to reassess the WDS impact on the GEE of the western regions. The DID model is as follows: 7

𝐺𝐸𝐸𝑖𝑡 = 𝛼0 + 𝛽1 𝑤𝑒𝑠𝑡_𝑖𝑑𝑖𝑡 + 𝛽2 𝑤𝑒𝑠𝑡_𝑡𝑖𝑡 + 𝛽3 𝑤𝑒𝑠𝑡𝑖𝑡 + 𝛿𝑖𝑡𝑘 ∑ 𝑍⃗𝑖𝑡𝑘 + 𝜀𝑖𝑡

(7)

𝑘=1

where west_id and west_t are the regional and temporal dummy variables, respectively. Only if region i belongs to the treatment group does west_id=1 exist. When time t is before 2000, west_t equals 0, otherwise west_t equals 1. west is the interaction term of west_id and west_t. ⃗𝑍⃗ is a series of control variables including infrastructure inf, investment inv, government intervention gov, urbanization urban, industrial structure indus, economic development level eco, and labor quality hum. We focus on β3. If β3 is significantly positive, it shows that the WDS can improve the GEE of the western regions. In addition, the pool OLS and fixed-effect OLS are used for a more reliable estimation result. Because the explanatory variable GEE varies between 0 and 1, therefore the parameter may be close to 0 when the OLS is used for regression (Greene, 1981). To obtain more robust results, the panel Tobit model is used. Moreover, because of endogeneity and the continuity of economic activities, this study also uses the system-GMM to estimate the formula (7). The lag period is relaxed to two periods when using the system-GMM estimation.

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3.1.3 Linear mechanism test

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In addition to evaluating the WDS effect on the GEE of western regions, we will further analyze the formation mechanism of this effect. This is conducive not only to an in-depth understanding of the policy effect of WDS but can also provide a clear policy path for sustainable development and low carbon emissions in other resource-based regions. Several policies have been introduced to attract talent to the western regions since the Chinese government “10-year Plan for Talent Development in the Western Region” in 2002. These policies not only show the importance and urgency of attracting talent to the western regions but also provide an avenue to investigate the interregional flow of research and development personnel (RDP). Extant studies have shown that an interregional flow of RDP promotes regional economic growth (Bai et al., 2017) and the spatial spillover of knowledge, thereby enhancing regional innovation capacity. The positive role of technological innovation in energy consumption, energy conservation, and environmental protection has already been proven (Costa-Campietal, 2015; Li et al., 2019). In addition, innovation can upgrade the industrial structure (Zhuo and Deng, 2018) and promote urbanization (Liu et al., 2016). In turn, the inflow of innovation factors improves the quality of local labor. Theoretically, a higher proportion of secondary industries will result in more environmental pollution. Wang et al. (2019) stated that urbanization is an important factor affecting per capita energy consumption. A higher quality labor force often provides the knowledge to promote energy-saving technology. Therefore, we describe the mechanism as follows: The WDS promotes upgrading of the industrial structure, urbanization, and improvement of labor quality by guiding innovation factors to the western regions, ultimately achieving green economic development. In order to verify the validity of this conduction mechanism, the following test steps are constructed: 4

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𝑀𝑖𝑡 = 𝛼0 + 𝛽1 𝑟𝑑𝑓𝑖𝑡 + 𝛿𝑖𝑡𝑘 ∑ 𝑍⃗𝑖𝑡𝑘 + 𝜀𝑖𝑡

(8)

𝑘=1

4

𝑀𝑖𝑡 = 𝛼0 + 𝛽2 𝑟𝑑𝑓𝑖𝑡 + 𝛽3 𝑟𝑑𝑓_𝑤𝑒𝑠𝑡𝑖𝑡 + 𝛿𝑖𝑡𝑘 ∑ 𝑍⃗𝑖𝑡𝑘 + 𝜀𝑖𝑡

(9)

𝑘=1

4

𝐺𝐸𝐸𝑖𝑡 = 𝛼0 + 𝛽4 𝑤𝑒𝑠𝑡𝑖𝑡 + 𝛽5 𝑀_𝑤𝑒𝑠𝑡𝑖𝑡 + 𝛿𝑖𝑡𝑘 ∑ 𝑍⃗𝑖𝑡𝑘 + 𝜀𝑖𝑡

(10)

𝑘=1

In formulas (8)-(10), rdfit represents the inflow of innovation factors; M refers to industrial structure indus, urbanization urban and labor quality hum; and ⃗⃗⃗ 𝑍 contains a series of control variables. When the core explanatory variables are removed, the set of control variables includes infrastructure inf, investment inv, government intervention gov, and economic development level eco. rdf_west is the interaction item of rdf and west. M_west is the interaction term of M and west. Formula (8) tests whether the interregional flow of innovation factors can promote an

Journal Pre-proof upgrading of the industrial structure, urbanization, and improved labor quality. If β1 is significantly positive, it indicates that these relationships are valid. Formula (9) examines whether the WDS influences M by enlarging the interregional flow of innovation factors. If β3 is significantly positive, it means that this relationship is valid. Formula (10) verifies whether the WDS impact on regional GEE depends on the upgrading of the industrial structure, urbanization, and improved labor quality. If β5 is significantly positive, it indicates that this relationship also is valid. Only if β1, β3, and β5 are simultaneously significantly positive can we affirm that the mechanism is established. That is to say, the WDS first guides the interregional flow of innovation factors, the inflow factors then promote upgrading of the industrial structure, urbanization, and improvement of labor quality, and ultimately the GEE of the western regions is raised. 3.1.4 Panel Smoothing Transformation Regression model

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The inflow of innovation factors leads to factor agglomeration, forming a congestion effect when the inflow scale exceeds the regional resource-carrying capacity (Brakman et al., 1996). In western China, the resource-carrying capacity is extremely limited because of the underdeveloped infrastructure and technology, meaning that an excessive agglomeration of innovation factors may have a negative impact on economic activities. Therefore, this study argues that there should be a moderate interval for the inflow scale of innovation factors under the influence of the WDS. Only within this interval can the transmission mechanism be fully integrated, thus effectively promoting green development. To verify this argument, this study uses a PSTR model to calculate the appropriate inflow scale of innovation factors during the WDS implementation since 2000. The PSTR model is a general form of the Hansen Panel Threshold model and uses a logistic function to transform systems smoothly. Therefore, the sudden change of a system in panel threshold model can be avoided. Using industrial structure upgrading as an example, we set the model as follows: 4

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𝐺𝐸𝐸𝑖𝑡 = 𝛼0 + 𝛽1 𝑖𝑛𝑑𝑢𝑠𝑖𝑡 + 𝛽2 𝑖𝑛𝑑𝑢𝑠 ∗ 𝑟𝑑𝑓𝑖𝑡 [1 + 𝑔(𝑟𝑑𝑓𝑖𝑡 ; 𝛾𝑗 ; 𝑐𝑗 )] + 𝛿𝑖𝑡𝑘 ∑ 𝑍⃗𝑖𝑡𝑘 + 𝜀𝑖𝑡

(11)

𝑘=1

where g(rdfit;j, cj) is a continuous transformation function with the transformation variable rdf. The function is determined by the following logistic function: 𝑚

𝑔(𝑟𝑑𝑓𝑖𝑡 ; 𝛾𝑗 ; 𝑐𝑗 ) = [1 + exp (−𝛾 ∏ 𝑟𝑑𝑓𝑖𝑡 − 𝑐𝑗 )]

−1

, γ∅0 且𝑐1 < 𝑐2 < ⋯ < 𝑐𝑚

(12)

𝑗=1

where  is the smooth parameter that is used to measure the smoothness of the transformation and the transformational speed between different systems and cj is the location parameter of the transformation function. When m=1 and , the model degenerates into the traditional panel threshold model. When g(rdfit;j, cj) has a smooth transition between 0 and 1, it means a smooth transition between low regime (g(·)=0) and high regime (g(·)=1). The existence of nonlinear features is firstly tested. Following the test path of Gonzalez et al. (2005), the first-order Taylor expansion at g(.)=0 is needed to construct the auxiliary regression equation:

Journal Pre-proof

𝐺𝐸𝐸𝑖𝑡 = 𝜕0 + 𝛽0 𝑖𝑛𝑑𝑢𝑠 ∗ 𝑟𝑑𝑓𝑖𝑡 + 𝑖𝑛𝑑𝑢𝑠 ∗ 𝑟𝑑𝑓𝑖𝑡 (𝜃1 𝑟𝑑𝑓𝑖𝑡 + 𝜃2 𝑟𝑑𝑓2𝑖𝑡 + ⋯ + 𝜃𝑚 𝑟𝑑𝑓𝑚 ) + 𝜀∗𝑖𝑡 𝑖𝑡

(13)

where 𝜀𝑖𝑡∗ = 𝜀𝑖𝑡 + 𝑅𝑚 𝛽1′ 𝑍⃗𝑖𝑡𝑘 ; Rm is the remainder of Taylor expansion3; and 𝜃1 ,𝜃2 ,⋯,𝜃𝑚 is the existential test parameter of system transformation. We then construct a progressively equivalent LM value (obeys χ2 distribution), an LMF value (obeys F statistic), and a pseudo-LTR statistic to test the parameters in the auxiliary regression equation. If 𝜃1 =𝜃2 =𝜃𝑚 =0, it indicates that the model is linear and the PSTR model is not suitable. On the contrary, a “residual nonlinear effect test” is required to test whether there is only one transformation function (H0: r=1), or at least two transformation functions (H0: r=2) and so on.

3.2 Variable measurement

of

3.2.1 Green economic efficiency

re

-p

ro

The traditional DEA model ignores the slack of input or output, which leads to some bias in evaluating the decision-making unit (DMU) productivity. Then Tone (2001) improved the traditional DEA model and proposed the SBM model. The SBM-DEA model can avoid the deviation of radial and angle. The efficiency with the undesirable outputs can also be measured by this model (Guo et al., 2011). Therefore, this study chooses the SBM-DEA model to calculate GEE of China’s provinces. The model is as follows:

1 𝑚 𝑠𝑖− ∑ 𝑚 𝑖=1 𝑥𝑖0 min 𝜌 = 𝑏 + 1 𝑞1 𝑠𝑟 2 𝑠𝑡 ∑𝑞𝑡=1 1 + 𝑞 + 𝑞 (∑𝑟=1 + 𝑦0 𝑏𝑡0 ) 1 2

(14)

na

lP

1−

𝑛

Jo ur

𝑥0 = ∑ 𝑥𝑗 𝜆𝑗 + 𝑠 − (𝑖 = 1, ⋯ , 𝑚)

s. t.

𝑗=1 𝑛

𝑦0 = ∑ 𝑦𝑗 𝜆𝑗 − 𝑠 + (𝑟 = 1, ⋯ , 𝑞1 ) 𝑗=1 𝑛

(15)

𝑏0 = ∑ 𝑏𝑗 𝜆𝑗 + 𝑠 𝑏 (𝑡 = 1, ⋯ , 𝑞2 )

{

𝑗=1 − + 𝑏− 𝜆𝑗 , 𝑠𝑖 , 𝑠𝑟 , 𝑠𝑡

≥ 0 (𝑗 = 1, ⋯ , 𝑛)

In formula (14) (15), ρ represents the GEE need to be calculated. j represents each DMU. n and m are respectively the number of DMU and input factors. q1 and q2 represent desirable output + 𝑏 and undesirable output. 𝑠− 𝑖 ,𝑠𝑟 ,𝑠𝑡 is the slack variable of input, desirable output and undesirable output respectively.𝜆𝑗 is the intensity variable. xj, yj, bj are the m-dimensional input variables of the jth DMU. x0, y0, b0 represents the input variables, output variables and undesirable output variables of DMU0.

3

Due to space limitations, see the description of Colletaz and Hurlin (2006) and Wang et al. (2019).

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1) Input variables Referring to many studies (Feng et al., 2018; Chen et al., 2019; Liu and Xin, 2019), we adopt regional total employees as the labor input L. The regional productive capital stock K is calculated by using perpetual inventory method which proposed by Goldsmith. The formula is as follows:

Kt= Kt-1(1 - 𝛿 t) + It

(16)

ro

of

In the above formula, Kt, δt and It represent capital stock, depreciation rate and current investment respectively. This study adopts the Total Investment in Fixed Assets as the current investment. To ensure the continuity and comparability of data, the fixed assets investment price index is used to deflate the Total Investment in Fixed Assets. Now the key is how to determine the capital stock of the base period. This study calculates the base-period capital stock by using the Modified Initial Period Capital Stock Growth Rate Method, which proposed by Reinsdorf and Cover (2005). The formula is: (17)

-p

K0= I0(1 + g)/(g + 𝛿)

2) Output variables

na

lP

re

In the above formula, K0, I0 and g represent the base-period capital stock, capital investment and the geometric average growth rates of fixed-price investment respectively. In addition, this paper sets the capital depreciation rate as 9.6% (Zhang et al., 2004). Production and energy consumption are inseparable. This study takes energy as an important input factor into production function. Energy input E is expressed by equivalent energy consumption which converted by standard coal method.

Jo ur

Measuring the GEE need to consider both desirable and undesirable output (Li et al., 2016; Wu et al., 2017). We use regional GDP to denote desirable output. In order to eliminate the impact of price fluctuations and ensure the comparability of data, this study uses GDP price index to deflate nominal GDP. The undesired output is represented by industrial waste gas, wastewater and solid waste. We adopt the total emission of industrial waste gas, industrial waste water and industrial solid waste to measure the above three variables. Using the above method, we have calculated the GEE of all of China’s provinces from 1995 to 2016. The regional GEE average is illustrated in Fig. 3.2.

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Beijing Xinjiang 1.00 Ningxia 0.90 Qinghai 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00

Gansu Shanxi Yunnan Guizhou

Tianjin Hebei Shanxi Nner Mongolia Liaoning Jilin Heikongjiang

Sichuan

Shanghai

Chongqing

Jiangsu

Hainan

Zhejiang

Guangxi

Anhui

Hubei

Henan

Fujian Jiangxi Shandong

of

Guangdong Hunan

na

3.2.2 Covariate

lP

re

-p

ro

Fig. 3.2 Mean of green economic efficiency The average GEE of Beijing, Shanghai, Guangdong, and other regions reaches above 0.9, meaning these areas have achieved positive economic development under their environmental constraints. Meanwhile, the GEE of Sichuan, Guizhou, Shanxi, Gansu, Qinghai, Ningxia, Xinjiang, and other western regions is generally lower than 0.6. This phenomenon is consistent with China’s reality in which most of the western regions are resource-based and weak in technology accumulation. Thus, their economic growth is deeply dependent on resource input and energy consumption, inhibiting the improvement of the GEE.

Jo ur

The influence of some important factors are controlled to obtain more accurate estimate results. As an “external input,” infrastructure has a significant impact on regional economic yield and productivity (Aschauer, 1989). Infrastructure also can promote the interregional flow of knowledge, accelerate technology spillover, and indirectly improve emission reduction technology. Investment has a direct impact on regional economic yield. Li and Lin (2017) indicated that government intervention affects the GEE. In addition, economic development not only requires much energy consumption but also can provide material basis for controlling pollution. The secondary industry generally produces waste that causes environmental problems or pollution in general. Wang et al. (2019) stated that urbanization is an important factor in per capita energy consumption and can be used as a way to control energy consumption. A higher labor quality can provide knowledge reserves that will improve energy-saving technology. WDS implementation enhances the interregional flow of R&D elements that can promote economic growth and spur innovation (Bai and Jiang, 2015). Therefore, it is necessary to control for these variables. Specifically, this study uses the distance covered by roads and railways per square kilometer to represent infrastructure inf. The proportion of fixed assets investment in GDP is used to measure investment level inv. The proportion of government budget expenditure in GDP is used to measure government intervention gov. The natural logarithm of GDP per capita is used to measure the level of economic development eco. The proportion of tertiary industry output in GDP is used to measure the industrial structure indus. The proportion of urban population in the total population is used to measure the level of urbanization urban.

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3.2.3 Core explanatory variable

𝑙𝑛𝑁𝑗𝑡 ∗ 𝑙𝑛𝑃𝐺𝐷𝑃𝑖𝑡 𝐷𝑖𝑗

ro

𝑓𝑟𝑑𝑝𝑖𝑗𝑡 =

𝑛−1

(18)

(19)

re

-p

𝑓𝑟𝑑𝑝𝑗𝑡 = ∑ 𝑓𝑟𝑑𝑝𝑖𝑗𝑡 𝑗=1

of

All levels of China’s governments have been encouraging young people to support western construction during the WDS implementation. For example, “The Plan for University Students Serving the West Voluntarily” was proposed in 2003. Because college students often have high levels of knowledge and motivation to be innovative, the inflow of these elements may become important if the WDS is to affect the western GEE. Therefore, this study investigates the impact mechanism from the perspective of interregional flow of R&D elements. Given that the RDP is the main carrier of knowledge, we focus on the interregional flow of RDP frdp. The gravity model, which has become important in the analysis of the flow of interregional factors (Bui and Chen, 2017), is used to measure the interregional flow of RDP. The formulas are as follows:

Jo ur

3.3 Data sources

na

lP

where frdpijt represents the scale of RDP flowing from region j to region i in period t; Njt represents the number of RDP in region j; and Dij is the central location distance between i and j that is calculated based on the electronic map provided by the National Geographic Information System Website. When the RDP in region j is excessive, an outward thrust is formed. The improvement of living standards in i will be attractive to RDP in j region. However, the distance between the two regions will restrict the mobility of RDP. frdpit is the total RDP flowing into region i in period t.

Our data covers the period 1996-2017. Most are collected from the China Statistical Yearbook, the China Statistical Yearbook on Science and Technology, the China Statistical Yearbook on Environment, and the Statistical Data Compilation of New China in the Past 60 Years. This study also collects statistical yearbooks and bulletins for the provinces, districts, and municipalities. Tibet is not included because of a lack of comprehensive data. Since Chongqing’s statistics were combined with those of Sichuan until 1997, when it was designated as a provincial-level municipality, this study separates Chongqing’s relevant indicators from those of Sichuan in 1995 and 1996 by consulting the local statistical data of Chongqing.

4. Empirical results 4.1 Results of baseline analysis The SCM is used to investigate the WDS impact on regional GEE. To clearly evaluate the WDS effect on different regions without concealing individual heterogeneity, a separate synthesis control is constructed for each treatment group instead of the past practice of merging all treatment

Journal Pre-proof

lP

re

-p

ro

of

groups. We choose the weight matrix W* to minimize the mean square error of GEE between the control group and the treatment group before WDS implementation. Finally, the net effect of the WDS on the GEE of the treatment group is described.

Jo ur

na

Fig. 4.1 The GEE of treatment group and control group As shown in Fig. 4.1, the location of the vertical dashed line represents the first year of the WDS. The red solid line indicates the actual trend of change in GEE in different regions. The black dotted line represents the trend of change in GEE in synthetic control regions. There is a large gap between the synthetic value and the actual value in Inner Mongolia, Guangxi, Chongqing, Gansu, and Qinghai before 2000, indicating that the later estimates of these five regions are unreasonable. Because we cannot judge whether the GEE difference is caused by a fitting error or by the WDS implementation, these five regions are then eliminated. In contrast, the solid red lines of Sichuan, Guizhou, Yunnan, Shaanxi, Ningxia, and Xinjiang approximately coincide with the black dotted lines before 2000, indicating that these six regions have better fit with the synthetic control regions. Therefore, the evaluation results in these six regions are highly reliable for detailed analysis. As shown in Fig. 4.2, to further visualize the WDS impact on the GEE of the six regions, this study illustrates the changing trend of the difference between the real GEE and the synthetic GEE.

re

-p

ro

of

Journal Pre-proof

Jo ur

na

lP

Fig. 4.2 Net effect of WDS on GEE The solid line of Sichuan, Guizhou, Yunnan, Shanxi, Ningxia, and Xinjiang fluctuated near the horizontal dotted line before 2000, meaning the synthetic control group of these six areas better fits the actual values. While the effects are obviously different among regions, Fig. 4.2 illustrates that the WDS has played a positive role in improving GEE. Yunnan’s real GEE is lower than that of the synthetic control group after 2009 but its solid red line is obviously above the black dotted line in the first nine years after WDS implementation. Therefore, the WDS has had a positive impact on Yunnan’s GEE though this effect has declined over time. In addition, the real GEE of Ningxia was lower than that of the synthetic control group at the beginning of the WDS but improved significantly after 2010. The GEE of Sichuan, Guizhou, Shaanxi, and Xinjiang is consistently significantly higher than that of the synthetic group after WDS implementation, indicating that WDS implementation has significantly improved the GEE of these four regions. These results prove that WDS implementation has helped resource-based regions reduce carbon emissions and achieve sustainable development.

4.2 Robustness test of baseline analysis To further test the reliability of the baseline analysis, the DID method is used to reassess the WDS impact on the GEE of the western regions. The results are shown in Table 4.1. Table 4.1 Results of robustness test Variables

The Pool - OLS

FE - OLS

Tobit

SYS - GMM

(1)

(2)

(3)

(4)

0.756***

0.681*** (15.57) 0.040

L1. GEE west_id

0.269***

0.454***

Journal Pre-proof

gov eco rdf indus urban

(3)

(4)

(5.56) 0.058* (1.77) 0.146** (3.18) 0.277*** (9.25) 0.610*** (19.03) 0.005 (0.22) 0.233*** (4.49) 0.238*** (9.44) 0.135*** (4.61) 0.131*** (3.21) 0.170*** (3.56)

(6.16) 0.008 (0.26) 0.153*** (3.91) 0.143*** (4.32) 0.441*** (12.90) 0.057* (1.92) 0.425*** (6.20) 0.107* (1.71) 0.043 (1.23) 0.007 (0.18) 0.130 (1.88)

(3.91) 0.003 (0.08) 0.240*** (5.07) 0.045 (0.92) 0.263*** (5.34) 0.094** (2.55) 0.588*** (6.45) 0.096 (0.66) 0.089* (1.87) 0.080 (1.27) 0.018 (0.19)

(0.33) 0.021*** (3.12) 0.043* (1.83) 0.026* (2.09) 0.157*** (5.18) 0.027 (0.61) 0.076 (0.81) 0.013 (0.14) 0.075** (2.57) 0.091 (0.86) 0.143*** (5.11) 0.002 0.810 1.000

660

630

0.760 660

Jo ur

AR (1) AR (2) Sargan Adj. R 2 N

(2)

na

hum

(1)

of

inv

SYS - GMM

ro

inf

Tobit

-p

west

FE - OLS

re

west_t

The Pool - OLS

lP

Variables

0.666 660

Note: *, **, and *** indicate that the statistical value is significant at the 10%, 5%, and 1% levels, respectively. The AR(1), AR(2), and Sargan tests are provided with p values. L1. represents the first lag phase of variables.

The coefficients of west in column (1)-(4) are significantly positive, indicating that the results of the SCM are credible. WDS implementation has significantly improved the GEE of the western regions.

4.3 Results of the linear mechanism analysis We then take the interregional flow of innovation factors to investigate the mechanism, which can be described as follows: The WDS promotes upgrading of the industrial structure, urbanization, and improvement of labor quality by guiding innovation factors to the western regions, ultimately achieving green economic development. The existence of the mechanism is further tested and the results are shown in Table 4.2. Table 4.2 Results of linear mechanism analysis Variables

The first step

The second step

The third step

Journal Pre-proof

rdf

Indus

urban

hum

Indus

urban

hum

GEE

GEE

GEE

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

0.620***

0.482**

0.265***

0.712***

0.541***

0.289***

(3.96)

(2.51)

(3.58)

0.124

0.006

0.719***

(0.65)

(0.07)

(3.72)

rdf_west

(4.59)

(2.80)

(3.88)

0.148***

0.094**

0.038**

(4.66)

(2.37)

(2.50)

west

indus_west

0.324

*

(1.72) 0.219**

urban_west

(2.46)

0.008

0.071*

0.072

(0.57)

(0.90)

(0.46)

(1.93)

(1.57)

(3.17) gov

0.058

**

(2.25) 0.972

Adj. R

2

N

(0.47) 0.021 (0.65) 0.085

0.068

0.101

(4.49) 0.087

***

(3.18) 0.080

(7.11) 0.345

***

***

***

(3.12) 1.093

***

0.093**

0.062

0.016

(1.18)

(2.18)

(1.42)

(0.37)

0.019

(0.49) 0.035

(1.09) 0.162

0.068

***

(4.49) 0.081

***

(6.54) 0.313

***

(7.77)

(0.55)

(5.81)

(8.69)

(1.03)

(5.19)

0.895

0.843

0.977

0.899

0.844

0.977

660

660

660

660

660

660

0.302

***

(6.01) 0.099

***

(2.72)

(5.80) 0.090

**

(2.49)

0.271*** (5.72) 0.099*** (2.79)

***

0.728***

(9.27)

(10.01)

(10.96)

660

660

660

0.601

***

0.280

***

0.653

na

eco

***

0.019

***

-p

0.102

***

(4.82)

0.021

ro

0.040

re

inv

0.020

lP

inf

0.975***

of

hum_west

Note: *, ** and, *** indicate that the statistical value is significant at the 10%, 5%, and 1% levels, respectively.

Jo ur

Columns (1)-(6) are estimated using a fixed effect OLS. Columns (7)-(9) are estimated using the panel Tobit method. In columns (1)-(3), the coefficients of rdf are 0.620, 0.482, and 0.265, respectively, passing the significance test and indicating that the interregional flow of innovation factors can significantly promote upgrading of the industrial structure, regional urbanization, and improvement of labor quality. In columns (4)-(6), the coefficient and t statistic of rdf are larger than in columns (1)-(3). The coefficient of rdf_west is also significantly positive, meaning the WDS enlargers the inflow of innovation factors. In columns (7)-(9), the coefficients of indus_west, urban_west, and hum_west are 0.324, 0.219, and 0.975, respectively, passing the significance test and indicating that the WDS impact on regional GEE is highly dependent on upgrading the industrial structure, urbanization, and improvement of labor quality.

4.4 Results of the nonlinear mechanism analysis The inflow of innovation factors leads to factor agglomeration and this may form a congestion effect when the inflow scale exceeds the regional resource-carrying capacity (Brakman et al., 1996). Therefore, there should be a moderate interval for the inflow scale of innovation factors under the influence of WDS. Only within this interval can the transmission mechanism be fully integrated, thus effectively promoting green development. The PSTR model is used to calculate the appropriate inflow scale of innovation factors during WDS implementation. The

Journal Pre-proof existence of nonlinear features is firstly tested and the results are shown in Table 4.3. Table 4.3 Results of nonlinear mechanism analysis Industrial structure upgrading m=1

m=1

***

65.53

m=2

***

102.25***

72.44

49.32

LMF

16.02***

4.67***

13.67***

5.53***

13.93***

13.35***

86.08*** 2.22 0.33 2.24

55.16*** 5.56 0.41 5.62

75.21*** 8.39 1.28 8.54

64.09*** 15.59 1.19 16.12

4.78 4.58

4.56 4.34

76.43*** 17.56*** 2.78** 18.23*** 3.08 0.44 3.10 5.01 4.70

132.87*** 26.04*** 2.08** 27.55*** 13.32 0.98 13.70 4.93 4.58

of

56.31

m=1

***

LM LRT Tests LM r = 1 LMF LRT Tests LM r = 2 LMF LRT Tests AIC BIC

64.64

Labor quality

m=2

***

ro

r=0

m=2

***

Urbanization

4.86 4.66

4.56 4.34

-p

Note: *, ** and, *** indicate that the statistical value is significant at the 10%, 5%, and 1% levels, respectively.

Jo ur

na

lP

re

When r=1, m=1, or m=2, LM, LMF, and LRT reject the null hypothesis H0 (r=0) at the 1% significance level, meaning that the panel data are heterogeneous and indicating that the inflow scale of innovation factors has a nonlinear effect on the transmission mechanism. The nonlinear residual test of the PSTR model shows that whether m=1 or m=2, the null hypothesis H0(r=1) cannot be rejected if we regard industrial structure upgrading and urbanization as the transmission paths. This indicates that in these two transmission processes, the PSTR model contains only one nonlinear transformation function. In terms of labor quality, the null hypothesis H0(r=2) is accepted and indicates that the PSTR model contains two nonlinear transformation functions. The location parameter m must be determined by AIC and BIC values. When m=1, the AIC and BIC values of the three transmission processes are all less than those of m=2. Therefore, the optimal location parameter is m=1 in the three transmission processes. After determining the relevant parameters, a grid point search method is used to estimate the coefficient parameters. The fixed effect is first eliminated by mean excision. Then the parameters are estimated through a grid point search. The optimal parameter estimation is obtained by minimizing the sum of squared residuals. The results are shown in Table 4.4. Table 4.4 Estimation results of Panel Smooth Transformation Regression model indus

Variables (1)

inv

1.031

(2) **

(2.52)

gov

1.563

*

(1.69)

eco

0.893

urban

**

(2.53)

4.471

(3) ***

(3.81) 11.502

***

(3.49) 4.695

***

(4.29)

(4)

0.118 (1.36) 1.050

hum

***

(9.36) 0.136

**

(2.42)

53.802

(5) ***

(4.94) 20.581

***

(5.08) 13.769

***

(3.17)

2.299

(6) **

(2.55) 0.088

(7)

0.163

*

(1.84) 0.190

***

2.433** (2.89) 0.272***

(0.74)

(3.60)

(3.09)

0.177

0.389

*

0.287*

(0.55)

(1.71)

(1.68)

Journal Pre-proof indus

Variables (1)

inf

0.443

indus

urban (2)

***

hum

(3)

0.762

**

(4.13)

(2.08)

0.027

4.284***

(0.85)

(8.26)

0.206

urban

(4) ***

6.168

(5) ***

(4.82)

(4.69)

0.449***

1.259

(5.01)

(0.28)

0.010

hum

0.036

0.329

(1.26)

(4.76)

0.229*

0.439*

(1.67)

(1.94)

(1.91)

3.603*

2.076**

3.472**

(1.83)

(2.24)

(2.53)

(0.62)

9.998

13.414

-p

26.612

19.544

0.626

0.347

re

0.282

0.618

1.712

1.216

lP

1.698

0.490*

10.032

ro

rdf * hum * [(1 + g (rdfit;gj, cj)]

RSS

(0.23)

(6.00) 0.939***

Location parameter (c)

0.027

(3.06)

***

rdf * urban * [(1 + g (rdfit;gj, cj)]

Smooth parameter (g)

0.179

(7) ***

(0.07)

of

rdf * indus * [(1 + g (rdfit;gj, cj)]

(6)

N

Note: *, ** and, *** indicate that the statistical value is significant at the 10%, 5%, and 1% levels,

na

respectively.

4

Jo ur

Based on different smooth and location parameters, the transformation functions of the three transmission mechanisms are shown in Fig. 5.1.

2

2

Urban Transition Function

Indus Transition Function

10 8 6

0

12

140

10

120 Hum Transition Function

12

100

8 6 4

1

2

rdf

3

60 40 20

0 0

80

0

0

1

rdf

2

3

0

1

2

3

rdf

Fig.5.1 Transformation function The transformation functions of the three transmission mechanisms show gradual changes, indicating that the PSTR model is reasonable. Table 4.4 shows that if rdf is less than 0.282, the coefficient of the rdf * indus * [1+g(rdfit;gj,cj)] is negative and insignificant. This indicates that the inflow of innovation factors cannot improve the GEE of the western regions through an upgrade of the industrial structure. However, if rdf reaches 0.282, the coefficient of rdf* indus* [1+g(rdfit;gj, cj)] becomes positive and the transmission process based on industrial structure upgrading can operate smoothly. For

Journal Pre-proof urbanization, only when the rdf is less than 0.626 is the coefficient of rdf* urban* [1+g(rdfit;gj, cj)] significantly positive. When the rdf is less than 0.347, the coefficient of rdf* hum* [1+g(rdfit;gj, cj)] is 3.603 and the t statistic value is 1.83. If the value of rdf varies from 0.347 to 0.618, the coefficient of rdf* hum* [1+g(rdfit;gj, cj)] is 2.076 and the t statistic value reaches 2.24. When rdf exceeds 0.618, the coefficient of rdf* hum* [1+g(rdfit;gj, cj)] is negative. These results indicate that when the rdf varies between 0.347-0.618, the inflow of innovation factors can promote green development by effectively improving the labor quality.

5. Discussion 5.1 Discussion for baseline analysis and the robustness test

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Fig. 4.2 shows that the GEE in Sichuan, Guizhou, Yunnan, Shanxi, Ningxia, and Xinjiang fluctuated to some extent after WDS implementation. However, as stated in the results section, during the implementation period the positive effect of the WDS on GEE is still significant. The WDS has led to the relocation of traditional industries from the east to the west (Zhang et al., 2019). Governments at all levels have implemented many preferential policies to support the economic development of the western regions (Sims and Schiff, 2000; Demurger et al., 2002). The efforts have led to an economic boom (Zhao et al., 2015) that can help reduce carbon intensity (Zhang et al., 2019). Economic development can attract talent, thus promoting urbanization, upgrading the industrial structure, and accelerating technological progress. Urbanization directly decreases energy consumption (Xie et al., 2019). Industrial structure upgrading and technological progress reduce the negative impact of resource-based and heavy pollution industries on the environment. Therefore, the WDS ultimately improves the GEE of western regions in China. In addition, the results of the robustness test also support this conclusion However, the GEE of these six regions declined significantly after the implementation period. The trend has become increasingly obvious and is prominent in Yunnan, Shaanxi, and Xinjiang. There may be two reasons for this. First, China has accelerated the pace of economic development since 2000. The western regions have abundant resources that can provide energy security for China’s development. The projects of “West gas to east” and “West electricity to east” are typical cases. With the increase in energy demand and resource exploitation, the GEE of the western regions will inevitably decline. Second, the strategies of “Revitalizing the China Northeast” and of “Rising Central China,” initiated in 2003 and 2006, diluted the financial and policy support from the central government, thus weakening the WDS impact on western regions.

5.2 Discussion for linear mechanism analysis The success of the WDS depends on whether high-quality talent teams can be built in the western regions. The “Opinions on Further Strengthening the Construction of the Talent Team in the Western Region” was issued in 2007, following the “10-year Plan for Talent Development in the Western Region” of 2002. In addition, the central government has launched other policies such as “Three Supports and One Support Program for College Graduates” and the “Plan for University Students Serving the West Voluntarily.” These policies are aimed at attracting talent to the western regions.

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Fig.5.2 Mechanism map As shown in Fig. 5.2, the inflow of innovation factors improves the local innovation level and provides technical support for producing high-tech goods. In addition, the inflow of talent will also create demand for high-tech products, thus upgrading the regional industrial structure. The inflow of talent not only directly provides the population basis for urbanization and leads to scientific and technological achievements but also improves citizens’ quality of life, thus promoting urbanization indirectly. Urbanization can control the excessive growth of energy consumption (Wang et al., 2019). The inflow of innovation factors also improves the human capital of the local labor force and strengthens the competition among talented employees, thus providing knowledge reserves for improving energy-saving technology.

5.3 Discussion for nonlinear mechanism analysis The knowledge spillover effect and economies of scale are closely related to the inflow scale of R&D factors. Only when the inflow reaches a certain scale can the positive effect on the industrial structure appear. In addition, when the inflow scale of innovation talent is excessive, living space will be squeezed and urban resources will be overextended. Hence, an excessive talent inflow is not conducive to urbanization. However, in terms of labor quality, an appropriate inflow scale of innovation factors shows a U-shape. The reason is that only when the inflow of talent reaches a certain scale can the knowledge spillover take effect and thus improve the quality of the labor force. However, if talent is excessively concentrated, it will intensify competition and limit enthusiasm for work, thus ultimately reducing the quality of labor. The interval for the appropriate inflow scale of innovation factors is 0.347-0.618 for the western regions. In this interval, the inflow of innovation factors can promote green development simultaneously through the three transmission mechanisms. Based on the location parameters and

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the inflow scale of innovation factors in 2016, the western regions are divided according to the driving mechanism into three categories as shown in Fig. 5.3 and Table 5.1.

Fig. 5.3 Inflow scale of innovation factors in China’s western regions

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Table 5.1 Classification based on the inflow scale of innovation factors

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The inflow scale of innovation factors

Regions

The scale is too small (~ 0.347)

Xinjiang, Yunnan

The scale is suitable (0.347 ~ 0.618)

Guangxi, Ningxia, Qinghai, Guizhou, Sichuan, Inner Mongolia, Shaanxi, Chongqing

The scale is too large (0.618 ~)

Gansu

Unblocked paths Upgrading of industrial structure; Improvement of labor quality Upgrading of industrial structure; Urbanization; Improvement of labor quality Improvement of labor quality

Xinjiang and Yunnan should use the WDS as an opportunity to improve societal benefits and actively attract high-quality talents to promote regional green development from multiple transmission mechanisms. Gansu should activate the transmission mechanisms of industrial structure upgrading and urbanization. Gansu, which has an excess inflow of innovation factors,

Journal Pre-proof also should control the inflow of talent and instead focus on improving the quality of talent inflow. In addition, it should develop high-tech manufacturing and service industries and accelerate the transformation of resource-based sunset industries. Gansu should also provide the means necessary for urbanization. Activating transmission mechanisms scientifically and pertinently is conducive to improving the GEE of the western regions under the WDS.

6. Conclusions and policy implications 6.1 Conclusions

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6.2 Policy implications

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China is the largest developing country in the world and faces severe environmental pollution. Among all of China’s regional development strategies, the WDS has the longest history and the most influence. This study analyzes China’s WDS to find the balance between environmental protection and economic growth and narrow the regional development gap. This study finds that the GEE in the coastal areas of China is generally higher than that in the western regions. The WDS has a positive influence on regional GEE as a whole. However, the effect decays over time and the heterogeneity of regions. The WDS can improve the regional industrial structure, urbanization, and labor quality by guiding innovation factors into the western regions, thus ultimately promoting green development. Only if the inflow scale of the innovation factors varies on the interval (0.347, 0.618) will the transmission mechanisms have simultaneous positive effects on the GEE .

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Those results have important implications for policy-makers in promoting regional green and low-carbon development. (1) Focus on improving the quality of economic development in the western regions while strengthening economic cooperation with the eastern regions. The western regions need to enhance economic cooperation with eastern regions and share their resource superiority. It is essential for western regions to optimize their industrial structure and transform their resource advantages into industrial advantages. In addition, the government should encourage more high-tech and fewer polluting industries to transfer from the east to the west. (2) Reform the household registration system and remove the institutional obstacles hindering the interregional flow of innovation factors. Governments in the western regions should actively formulate plans to attract talent, establish funds for awards, and improve the living standard in western regions. It is necessary to establish incentives to continuously inspire enthusiasm in the labor force to effectively promote industrial structure upgrading, urbanization, and the quality of labor in the western regions. (3) Prudently formulate policies to attract talent and appropriately guide the inflow of innovative talent. For Xinjiang and Yunnan, the priority is actively attracting high-quality talent. For Gansu, where the inflow of innovation factors is excessive, the focus should be on introducing high-tech industries and accelerating the transformation of resource-based industries. As well, providing the necessary infrastructure to promote urbanization is an important measure. These efforts can activate the two important transmission mechanisms of industrial structure upgrading and urbanization, thus effectively promoting green development. (4) Avoid the policy conflicts between the WDS and other regional development strategies

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Acknowledgements

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This research is funded by National Social Science Foundation of China (18BJL083), Xinjiang Uygur Autonomous Region Natural Science Foundation Project (2017D01C031), Major project of Key Research Center of Humanities and Social Sciences of Xinjiang Uygur Autonomous Region (010116A03) and Graduate student "Silk Road" Innovation Fund Project of Xinjiang University (JGSL17002).

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Conflict of interest

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We don’t have any actual or potential conflict of interest including any financial, personal or other relationships with other people or organizations.

Journal Pre-proof Table 4.1 Results of robustness test Variables

The Pool - OLS

FE - OLS

Tobit

SYS - GMM

(1)

(2)

(3)

(4)

L1. GEE

gov eco rdf

na

indus urban

AR (1) AR (2) Sargan Adj. R 2 N

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hum

0.760 660

of

inv

0.666 660

0.756*** (3.91) 0.003 (0.08) 0.240*** (5.07) 0.045 (0.92) 0.263*** (5.34) 0.094** (2.55) 0.588*** (6.45) 0.096 (0.66) 0.089* (1.87) 0.080 (1.27) 0.018 (0.19)

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inf

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west

0.454*** (6.16) 0.008 (0.26) 0.153*** (3.91) 0.143*** (4.32) 0.441*** (12.90) 0.057* (1.92) 0.425*** (6.20) 0.107* (1.71) 0.043 (1.23) 0.007 (0.18) 0.130 (1.88)

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west_t

0.269*** (5.56) 0.058* (1.77) 0.146** (3.18) 0.277*** (9.25) 0.610*** (19.03) 0.005 (0.22) 0.233*** (4.49) 0.238*** (9.44) 0.135*** (4.61) 0.131*** (3.21) 0.170*** (3.56)

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west_id

660

0.681*** (15.57) 0.040 (0.33) 0.021*** (3.12) 0.043* (1.83) 0.026* (2.09) 0.157*** (5.18) 0.027 (0.61) 0.076 (0.81) 0.013 (0.14) 0.075** (2.57) 0.091 (0.86) 0.143*** (5.11) 0.002 0.810 1.000 630

Note: *, **, and *** indicate that the statistical value is significant at the 10%, 5%, and 1% levels, respectively. The AR(1), AR(2), and Sargan tests are provided with p values. L1. represents the first lag phase of variables.

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Table 4.2 Results of linear mechanism analysis The first step

Variables rdf

The second step

The third step

Indus

urban

hum

Indus

urban

hum

GEE

GEE

GEE

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

0.124

0.006

0.719***

(0.65)

(0.07)

(3.72)

0.620

***

(3.96)

0.482

**

(2.51)

0.265

***

0.712

(3.58)

rdf_west

***

(4.59) 0.148

0.541

***

0.289

(2.80)

***

(4.66)

0.094

***

(3.88)

**

0.038**

(2.37)

(2.50)

of

west

0.324

ro

indus_west

(2.18)

(0.46)

(1.93)

0.972

***

(0.47) 0.021

0.068

0.101

(4.49) 0.087

(0.65)

***

0.085

(0.55)

(7.11)

0.345

(3.18)

0.080

***

(5.81)

***

(3.12)

1.093

***

0.019 (0.49) 0.035 (1.09) 0.162

0.068

***

(4.49) 0.081

***

(6.54) 0.313

***

(8.69)

(1.03)

(5.19)

0.895

0.843

0.977

0.899

0.844

0.977

660

660

660

660

660

660

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(7.77)

0.019

***

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**

(2.25)

N

(1.18)

(0.90)

0.058

Adj. R 2

(1.57)

(0.57)

***

(4.82) 0.093

0.071

***

0.975***

0.021

0.008

(3.17)

eco

(2.46)

0.072

0.040

0.102

gov

0.219**

*

0.020

na

inv

re

hum_west

inf

(1.72)

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urban_west

*

0.302

**

***

(6.01) 0.099

***

(2.72)

0.016

(1.42)

(0.37)

0.280

***

(5.80) 0.090

**

(2.49)

0.271*** (5.72) 0.099*** (2.79)

***

0.728***

(9.27)

(10.01)

(10.96)

660

660

660

0.601

***

0.062

0.653

Note: *, ** and, *** indicate that the statistical value is significant at the 10%, 5%, and 1% levels, respectively.

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Table 4.3 Results of nonlinear mechanism analysis Industrial structure upgrading m=1

m=1

***

Labor quality

m=2

m=1

m=2 102.25***

49.32

LMF

16.02***

4.67***

13.67***

5.53***

13.93***

13.35***

86.08*** 2.22 0.33 2.24

55.16*** 5.56 0.41 5.62

75.21*** 8.39 1.28 8.54

64.09*** 15.59 1.19 16.12

4.78 4.58

4.56 4.34

76.43*** 17.56*** 2.78** 18.23*** 3.08 0.44 3.10 5.01 4.70

132.87*** 26.04*** 2.08** 27.55*** 13.32 0.98 13.70 4.93 4.58

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4.86 4.66

4.56 4.34

65.53

***

72.44

of

56.31

***

LM LRT Tests LM r = 1 LMF LRT Tests LM r = 2 LMF LRT Tests AIC BIC

64.64

***

ro

r=0

m=2

***

Urbanization

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urban

hum

Variables

gov

(2)

(3)

(4)

(5)

(6)

(7)

1.031**

4.471***

0.118

53.802***

2.299**

0.163*

2.433**

(2.52)

(3.81)

(1.36)

(4.94)

(2.55)

(1.84)

(2.89)

1.563

*

(1.69)

eco

0.893

**

(2.53)

inf

0.443

***

(4.13)

indus

0.027 (0.85)

11.502

***

1.050

(3.49) 4.695

0.136

(4.29)

**

0.206

13.769

6.168

(4.82)

(8.26) 0.449***

(4.69)

(5.01)

0.036 (1.26)

0.329

N

lP 26.612

0.282

Jo ur

Location parameter (c)

na

Smooth parameter (g)

1.698

0.190

0.272***

(0.74)

(3.60)

(3.09)

0.177

0.389

*

0.287*

(0.55)

(1.71)

(1.68)

0.010 (0.07)

0.179

***

0.027

(3.06)

(0.23)

0.490*

0.229*

0.439*

(1.67)

(1.94)

(1.91)

3.603*

2.076**

3.472**

(1.83)

(2.24)

(2.53)

(0.28)

(6.00)

rdf * urban * [(1 + g (rdfit;gj, cj)] rdf * hum * [(1 + g (rdfit;gj, cj)]

re

rdf * indus * [(1 + g (rdfit;gj, cj)]

***

0.088

***

1.259

-p

hum

RSS

***

ro

urban

***

(3.17)

***

***

***

(5.08)

(2.42)

**

(2.08) 4.284

20.581

(9.36)

***

0.762

***

of

inv

(1)

0.939***

10.032

(4.76)

(0.62)

9.998

13.414 19.544

0.626

0.347 0.618

1.712

1.216

Journal Pre-proof

Table 5.1 Classification based on the inflow scale of innovation factors The inflow scale of innovation factors

Regions

Unblocked paths

Xinjiang, Yunnan

The scale is suitable (0.347 ~ 0.618)

Guangxi, Ningxia, Qinghai, Guizhou, Sichuan, Inner Mongolia, Shaanxi, Chongqing

The scale is too large (0.618 ~)

Gansu

Upgrading of industrial structure; Improvement of labor quality Upgrading of industrial structure; Urbanization; Improvement of labor quality

Jo ur

na

lP

re

-p

ro

of

The scale is too small (~ 0.347)

Improvement of labor quality

Journal Pre-proof

Graphical Abstract

Jo ur

na

lP

re

-p

ro

of

How does China’s Western Development Strategy Affect Regional Green Economic Efficiency?

Journal Pre-proof

Highlights



The provincial green economy efficiencies of China are calculated.



The net eff ect of China’s Western Development Strategy (WDS) on regional green economy efficiency is quantitatively estimated via

The transmission mechanisms that WDS affect regional green

ro



of

Synthetic Control Method.

-p

economy efficiency are investigated from the perspective of the

lP

The optimal scale of innovation factors flowing into WDS’s regions is

na

calculated via the Panel Smooth Transition Regression model.

Jo ur



re

interregional flow of innovation factors.