Environmental Benefits of the West-East Natural Gas Transmission Project: Cross-Regional Energy Optimization and Transformation for Sustainable Development
Abstract
:1. Background of the Study
2. Literature Review
3. Research Hypothesis
4. Data Selection and Empirical Methods
4.1. Data Sources and Processing
4.1.1. Data Sources
4.1.2. Sample Selection
4.1.3. Data Processing
4.2. Model Design
4.2.1. Basic DID Model
4.2.2. Moderated Effects Model
4.3. Variable Description
4.3.1. Explanatory Variable
4.3.2. Core Explanatory Variables
4.3.3. Adjustment Variables
4.3.4. Control Variables
4.4. Summary and Limitations of the Research Methodology
4.4.1. Summary of Research Methodology
4.4.2. Limitations of Analytical Methods
5. Empirical Findings
5.1. Base DID Model Regression Results
5.2. Moderated Effects Model
5.3. Parallel Trend Testing: An Event Study Approach
5.4. Placebo Testing
5.5. Heterogeneity Analysis
5.5.1. Different Holdings
5.5.2. Different Regions
6. Research Findings and Policy Recommendations
6.1. Conclusions and Discussion of This Study
6.2. Policy Recommendations
6.3. Future Research Directions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
Abbreviation | Explanation |
BRICS | Brazil, Russia, India, China, and South Africa |
CO | Carbon Monoxide |
CSMAR | China Stock Market & Accounting Research Database |
DID | Difference-in-Differences |
ECK | Environmental Kuznets Curve |
ESG | Environmental, Social, and Governance |
GDP | Gross Domestic Product |
Ⅱ | Phase II |
LIN | Linear Interpolation |
WEG | West-East Gas Pipeline |
WEGP | West-East Gas Pipeline Project |
WEGT | West-East Gas Transmission Test |
WTO | World Trade Organization |
SO2 | Sulfur Dioxide |
NOX | Nitrogen Oxides |
PM2.5 | Particulate Matter 2.5 |
CO2 | Carbon Dioxide |
EIA | Environmental Impact Assessment |
R&D | Research and Development |
WENGT | West-East Natural Gas Transmission Project |
ER | Environmental Regulation |
CSDB | China Industrial Enterprises Database |
References
- Brandt, L.; Rawski, T.G.; Sutton, J. China’s industrial development. In China’s Great Economic Transformation; Cambridge University Press: Cambridge, UK, 2008; pp. 569–632. [Google Scholar]
- Wei, J.; Li, Z.; Wang, J.; Li, C.; Gupta, P.; Cribb, M. Ground-level gaseous pollutants (NO2, SO2, and CO) in China: Daily seamless mapping and spatiotemporal variations. Atmos. Chem. Phys. 2023, 23, 1511–1532. [Google Scholar] [CrossRef]
- Antwi, H.A.; Zhou, L.; Xu, X.; Mustafa, T. Progressing towards Environmental Health Targets in China: An Integrative Review of Achievements in Air and Water Pollution under the “Ecological Civilisation and the Beautiful China” Dream. Sustainability 2021, 13, 3664. [Google Scholar] [CrossRef]
- Liu, W.; Fan, W.; Hong, Y.; Chen, C. A study on the comprehensive evaluation and analysis of China’s renewable energy development and regional energy development. Front. Energy Res. 2021, 9, 635570. [Google Scholar] [CrossRef]
- Yu, Z.; Li, W.; Duan, H. New Energy Technology Innovation and Industry Carbon Emission Reduction Based on the Perspective of Unbalanced Regional Economic Development. Sustainability 2023, 15, 15991. [Google Scholar] [CrossRef]
- Gao, Q.; Zhang, X.; Yang, M.; Chen, X.; Zhou, H. Fuzzy decision-based optimal energy dispatch for integrated energy systems with energy storage. Front. Energy Res. 2021, 9, 809024. [Google Scholar] [CrossRef]
- Zhang, J.; Zheng, H.; He, W.; Huang, W. West-east gas pipeline project. Front. Eng. Manag. 2020, 7, 163–167. [Google Scholar] [CrossRef]
- Cao, X.; Zhang, H.; Huang, X.; Li, P. Can the Belt and Road Initiative reduce pollution in enterprises?—Evidence from quasi-natural experiments. Energy Rep. 2022, 8, 11683–11694. [Google Scholar] [CrossRef]
- Miao, S.; Tuo, Y.; Zhang, X.; Hou, X. Green fiscal policy and ESG performance: Evidence from the energy-saving and emission-reduction policy in China. Energies 2023, 16, 3667. [Google Scholar] [CrossRef]
- Sun, J.; Li, G.; Wang, Z. Optimizing China’s energy consumption structure under energy and carbon constraints. Struct. Change Econ. Dyn. 2018, 47, 57–72. [Google Scholar] [CrossRef]
- Zhang, H.; Miao, B.; Chen, X.Y. The Proposal of the “Dual Carbon” Goals and Its Opportunities and Challenges for “West-to-East Electricity Transmission”. China Mark. 2023, 28, 9–12. [Google Scholar]
- Chen, M.; Bai, Z.; Wang, Q.; Shi, Z. Habitat quality effect and driving mechanism of land use transitions: A case study of Henan water source area of the middle route of the south-to-north water transfer project. Land 2021, 10, 796. [Google Scholar] [CrossRef]
- Introduction to the “Thirteenth Five-Year Plan” for Energy Development (Part II). Energy Energy Conserv. 2017, 7, 1. [CrossRef]
- Zhang, Y.G.; Bai, Y.J. The Foundation and Path of Regional Collaborative Low-Carbon Development. China Econ. 2022, 17, 69–92. [Google Scholar]
- West-East Gas Pipeline: Green Progress. 2016, December 30. China Petroleum News Center. Available online: http://news.cnpc.com.cn/system/2016/12/30/001628190 (accessed on 19 April 2024).
- Li, C.H.; Xie, J. Study on the Relationship between Cross-Regional Energy Dispatch and Economic Growth—Evidence from the West-East Gas Transmission Project. J. Ind. Technol. Econ. 2015, 34, 32–37. [Google Scholar]
- Chen, Y.; Zhao, J.; Lai, Z.; Wang, Z. Exploring the effects of economic growth, and renewable and non-renewable energy consumption on China’s CO2 emissions: Evidence from a regional panel analysis. Renew. Energy 2019, 140, 341–353. [Google Scholar] [CrossRef]
- Zhao, X.; Zhang, X.; Li, N.; Shao, S.; Geng, Y. Decoupling economic growth from carbon dioxide emissions in China: A sectoral factor decomposition analysis. J. Clean. Prod. 2017, 142, 3500–3516. [Google Scholar] [CrossRef]
- Kai, C.; Xiayun, S.; Zhongwen, T. Chinese energy consumption and carbon emission intensity on EKC effects under the constraint of energy-saving and emission-reduction. Sci. Technol. Manag. Res. 2015, 35, 206–209. [Google Scholar]
- Wang, D.; Li, T. Carbon emission performance of independent oil and natural gas producers in the United States. Sustainability 2018, 10, 110. [Google Scholar] [CrossRef]
- Dong, K.; Sun, R.; Hochman, G. Do natural gas and renewable energy consumption lead to less CO2 emission? Empirical evidence from a panel of BRICS countries. Energy 2017, 141, 1466–1478. [Google Scholar] [CrossRef]
- Jia, R.; Fan, M.; Shao, S.; Yu, Y. Urbanization and haze-governance performance: Evidence from China’s 248 cities. J. Environ. Manag. 2021, 288, 112436. [Google Scholar] [CrossRef]
- Wu, J.; Cui, C.; Guo, X. Impacts of the West–East Gas Pipeline Project on energy conservation and emission reduction: Empirical evidence from Hubei province in Central China. Environ. Sci. Pollut. Res. 2022, 29, 28149–28165. [Google Scholar] [CrossRef] [PubMed]
- Qiao, G.Y.; Chen, X.W.; Zhang, Z.E.; Han, X.L.; Wang, X.; Liao, B.; Xiao, F.R. Mechanical properties of high-Nb X80 steel weld pipes for the second west-to-east gas transmission pipeline project. Adv. Mater. Sci. Eng. 2017, 7409873. [Google Scholar] [CrossRef]
- Yuan, H.; Zhu, C.L. Have National High-tech Zones Promoted the Transformation and Upgrading of China’s Industrial Structure? China Ind. Econ. 2018, 8, 60–77. [Google Scholar]
- Han, C.; Chen, Z.; Wang, Z. A Study on the Mechanism of Pollution Reduction Effects of Enterprises Under the Constraint of Energy-Saving Targets. China Ind. Econ. 2020, 10, 43–61. [Google Scholar]
- Ju, B.; Shi, X.; Mei, Y. The current state and prospects of China’s environmental, social, and governance policies. Front. Environ. Sci. 2022, 10, 999145. [Google Scholar] [CrossRef]
- Chen, W.; Wu, Y. China’s new environmental protection law and green innovation: Evidence from prefecture-level cities. Complexity 2021, 1–13. [Google Scholar] [CrossRef]
- Chen, S.Y. Energy Consumption, Carbon Dioxide Emissions, and Sustainable Development of Chinese Industry. Econ. Res. J. 2009, 44, 41–55. [Google Scholar]
- Wang, X.; Lu, X.; Zhou, N.; Xiao, J.; Chen, J. Does Environmental Regulation Affect Natural Gas Consumption? Evidence from China with Spatial Insights. Sustainability 2020, 12, 3354. [Google Scholar] [CrossRef]
- Fan, F.; Lian, H.; Liu, X.; Wang, X. Can environmental regulation promote urban green innovation Efficiency? An empirical study based on Chinese cities. J. Clean. Prod. 2021, 287, 125060. [Google Scholar] [CrossRef]
- Xu, B.; Chen, Y.; Shen, X. Development of clean energy, carbon dioxide reduction, and regional economic growth. Econ. Res. J. 2019, 54, 188–202. [Google Scholar]
- Lin, T. Cleaner Production Environment Regulation and Enterprise Environment Performance An Empirical Test based on Pollution Discharge Data of Industrial Enterprises. J. Beijing Inst. Technol. (Soc. Sci. Ed.) 2022, 24, 43–55. [Google Scholar]
- Gao, Y.; Zheng, J. Clearing the air through pipes? An evaluation of the air pollution reduction effect of China’s natural gas pipeline projects. Energy Policy 2022, 160, 112649. [Google Scholar] [CrossRef]
- Xu, S.; Klaiber, H.A. The impact of new natural gas pipelines on emissions and fuel consumption in China. Resour. Energy Econ. 2019, 55, 49–62. [Google Scholar] [CrossRef]
- Shao, C. How does import competition affect corporate environmental performance?—A quasi-natural experiment from China’s accession to the WTO. Econ. (Q.) 2021, 21, 1615–1638. [Google Scholar]
- Azam, A.; Rafiq, M.; Shafique, M.; Zhang, H.; Yuan, J. Analyzing the effect of natural gas, nuclear energy and renewable energy on GDP and carbon emissions: A multi-variate panel data analysis. Energy 2021, 219, 119592. [Google Scholar] [CrossRef]
- Li, R.; Su, M. The role of natural gas and renewable energy in curbing carbon emission: Case study of the United States. Sustainability 2017, 9, 600. [Google Scholar] [CrossRef]
- Xiao, Z.; Cai, C.; Wang, L.; Ma, Y. Climate change, environmental regulations, and firms’ efforts to reduce pollutant emissions. Front. Ecol. Evol. 2023, 11, 1050642. [Google Scholar] [CrossRef]
- Gong, M.; You, Z.; Wang, L.; Cheng, J. Environmental regulation, trade comparative advantage, and the manufacturing industry’s green transformation and upgrading. Int. J. Environ. Res. Public Health 2020, 17, 2823. [Google Scholar] [CrossRef] [PubMed]
- Dong, X.; Zhong, Y.; Liu, M.; Qin, C. Research on the impacts of dual environmental regulation on regional carbon emissions under the goal of carbon neutrality-the intermediary role of green technology innovation. Front. Environ. Sci. 2022, 10, 993833. [Google Scholar] [CrossRef]
- Nie, H.; Jiang, T.; Yang, R. Current status and potential problems of the use of China’s industrial enterprise database. World Econ. 2012, 35, 142–158. [Google Scholar]
- Li, H.; Tong, M.; Zhang, G.; Zhao, J. Research on the emission reduction effects of cross-regional energy scheduling in enterprises. Quant. Tech. Econ. Res. 2023, 40, 156–178. [Google Scholar]
- Deng, J.; Zhang, N.; Ahmad, F.; Draz, M.U. Local government competition, environmental regulation intensity and regional innovation performance: An empirical investigation of Chinese provinces. Int. J. Environ. Res. Public Health 2019, 16, 2130. [Google Scholar] [CrossRef] [PubMed]
- Wang, F.; Sun, Z. Does the environmental regulation intensity and ESG performance have a substitution effect on the impact of enterprise green innovation: Evidence from China. Int. J. Environ. Res. Public Health 2022, 19, 8558. [Google Scholar] [CrossRef] [PubMed]
- Chen, Z.; Kahn, M.E.; Liu, Y.; Wang, Z. The consequences of spatially differentiated water pollution regulation in China. J. Environ. Econ. Manag. 2018, 88, 468–485. [Google Scholar] [CrossRef]
- Li, C.; Liu, X.; Bai, X.; Umar, M. Financial development and environmental regulations: The two pillars of green transformation in China. Int. J. Environ. Res. Public Health 2020, 17, 9242. [Google Scholar] [CrossRef] [PubMed]
- Jin, H.; Chen, S. The impact of geographic distance on government supervision of corporate pollution emissions—Discussing the role of data technology in regulation. Quant. Tech. Econ. Res. 2022, 39, 109–128. [Google Scholar]
- He, X.; Teng, R.; Feng, D.; Gai, J. Industrial Robots and Pollution: Evidence from Chinese Enterprises. Econ. Anal. Policy 2024, 82, 629–650. [Google Scholar] [CrossRef]
- Liu, M.; Liu, L.; Xu, S.; Du, M.; Liu, X.; Zhang, Y. The Influences of Government Subsidies on Performance of New Energy Firms: A Firm Heterogeneity Perspective. Sustainability 2019, 11, 4518. [Google Scholar] [CrossRef]
Environmental Protection Dimension | Associated Words |
---|---|
Environmental protection objectives | Environmental Protection, Environmental Protection, Green, Clean, Low-carbon, Blue Sky, Green Water, Green Hills |
Target audience: environmental factors | Ecology, Air, Climate |
Target group: environmental pollution | Pollution, Sulfur Dioxide, Chemical Oxygen Demand, Haze, Particulate Matter, Carbon Dioxide, Energy Consumption, Bulk Coal, Coal Combustion, Emissions, Smuggling, Tailpipe Gas |
Environmental protection measures | Energy Saving, Emission Reduction, Desulfurization, Denitrification |
Research Step | Descriptive |
---|---|
Data sources | 1. Chinese Industrial Enterprise Database: data from the National Bureau of Statistics, covering state-owned and non-state-owned industrial enterprises with main business income of CNY 5 million and above. 2. Pollution Database of Chinese Industrial Enterprises: developed by the National Bureau of Statistics and the Ministry of Environmental Protection, covering data on the emission and treatment of 27 industrial pollutants. |
Data integration | 1. Match the Chinese Industrial Enterprise Database with the pollution database using enterprise-unique identifiers and years to form panel data. 2. Delineate the scope of cities along the route of the first line of the West-East Natural Gas Pipeline Project, and cross-check the data against the “China Natural Gas Pipeline Distribution Map 2013” and local government reports. |
Data processing | 1. Samples with negative or missing sales revenue, total assets, total liabilities, and total fixed assets are deleted. 2. Samples with fewer than eight employees are deleted. 3. Then, 2000 is used as the base period to deflate all nominal variables. 4. Samples of firms founded earlier than 1949 are deleted. |
Model selection | 1. The double-difference model (DID) is used to study the impact of the West-East Natural Gas Pipeline Project on the emission of air pollutants from industrial enterprises. 2. The regulation effect model is used to study the regulation effect of the strength of a local government’s environmental regulation on the emission reduction effect of the West-East Natural Gas Pipeline Project. |
Variable description | 1. Explained variables: air pollutant emission levels of industrial firms (lnSO2 and lnYC, log form). 2. Core explanatory variables: treat (whether it is a pathway city or not) and post (whether it is after full ventilation or not). 3. Moderating variables: local government’s strength of environmental regulation, calculated based on the frequency of environment-related keywords in the working report. 4. Control variables: firm year of the square term, logarithm of total firm assets, fixed asset ratio, debt ratio, holdings, and industry category. |
Main premise | 1. The West-East Natural Gas Pipeline Project has a significant emission reduction effect on industrial enterprises in cities along the route. 2. The increase in the intensity of environmental regulation will enhance the emission reduction effect. 3. There are differences in the emission reduction effect under different corporate governance structures. 4. There are significant regional differences, with the most significant emission reduction effect in the eastern coastal region. |
Outcome testing | 1. The parallel trend test is used to verify the validity of the double-difference model. 2. The placebo test is used to test the impact of other policy shocks. 3. Heterogeneity analysis is carried out to analyze the impact of different holding situations and zones on the effect of emission reductions. |
Variable | (1) | (2) | (3) | (4) | (5) | (6) | (7) |
---|---|---|---|---|---|---|---|
Did | −0.138 *** (0.020) [−0.177, −0.099] | −0.043 ** (0.019) [−0.081, −0.005] | −0.163 *** (0.019) [−0.200, −0.126] | −0.067 ** (0.019) [−0.105, −0.029] | −0.119 *** (0.020) [−0.158, −0.080] | −0.051 *** (0.019) [−0.089, −0.013] | 0.006 (0.016) [−0.026, 0.038] |
Year of business square | NO | NO | −5.54 × 10−5 *** (1.01 × 10−5) [−7.52 × 10−5, −3.56 × 10−5] | −3.96 × 10−5 *** (1.03 × 10−5) [−6.00 × 10−5, −1.92 × 10−5] | −3.88 × 10−5 *** (9.93 × 10−6) [−5.80 × 10−5, −1.96 × 10−5] | −3.46 × 10−5 *** (1.02 × 10−5) [−5.46 × 10−5, −1.46 × 10−5] | 1.90 × 10−5 ** (8.46 × 10−6) [2.43 × 10−6, 3.56 × 10−5] |
Total assets | NO | NO | 0.251 *** (0.004) [0.243, 0.259] | 0.232 *** (0.005) [0.222, 0.242] | 0.298 *** (0.005) [0.288, 0.308] | 0.244 *** (0.005) [0.234, 0.254] | 0.433 *** (0.004) [0.425, 0.441] |
Proportion of fixed assets | NO | NO | 0.366 *** (0.019) [0.329, 0.403] | 0.442 *** (0.021) [0.400, 0.484] | 0.315 *** (0.019) [0.278, 0.352] | 0.433 *** (0.021) [0.392, 0.474] | 0.289 *** (0.018) [0.254, 0.324] |
Debt ceiling | NO | NO | 0.369 × 10−4 (0.001) [−0.001, 0.002] | 0.101 *** (0.014) [0.073, 0.129] | 0.001 (8.79 × 10−4) [−0.001, 0.003] | 0.101 *** (0.014) [0.073, 0.129] | −7.86 × 10−6 (7.96 × 10−4) [−0.001, 0.001] |
Holdings | NO | NO | YES | YES | YES | YES | YES |
Industry sector | NO | NO | YES | YES | YES | YES | YES |
Year-fixed effect | NO | NO | NO | NO | YES | YES | YES |
City-fixed effect | NO | NO | NO | NO | YES | YES | YES |
Constant term (math.) | 9.545 *** (0.007) [9.531, 9.559] | 8.843 *** (0.007) [8.829, 8.857] | 6.256 *** (0.058) [6.142, 6.370] | 6.218 *** (0.057) [6.106, 6.330] | 6.243 *** (0.062) [6.121, 6.365] | 5.876 *** (0.060) [5.758, 5.994] | 5.597 *** (0.054) [5.491, 5.703] |
Adjusted R-squared | 0.018 | 0.013 | 0.165 | 0.135 | 0.172 | 0.135 | 0.163 |
Sample size | 351,665 | 336,678 | 315,033 | 302,317 | 315,033 | 302,317 | 348,952 |
Variable | (1) | (2) | (3) | (4) | (5) | (6) |
---|---|---|---|---|---|---|
−0.122 *** (0.027) [−0.175, −0.069] | 0.040 (0.028) [−0.015, 0.095] | −0.155 *** (0.028) [−0.209, −0.101] | −0.030 (0.029) [−0.086, 0.026] | −0.072 *** (0.029) [−0.129, −0.016] | −0.035 (0.030) [−0.094, 0.023] | |
Year of business squared | NO | NO | −5.31 × 10−5 *** (1.16 × 10−5) [−7.58 × 10−5, −3.04 × 10−5] | −2.83 × 10−5 *** (1.16 × 10−5) [−5.1 × 10−5, −5.64 × 10−6] | −3.39 × 10−5 *** (−1.14 × 10−5) [−5.63 × 10−5, −1.15 × 10−5] | −2.3 × 10−5 *** (1.15 × 10−5) [−4.56 × 10−5, −4.25 × 10−7] |
Total assets | NO | NO | 0.249 *** (0.005) [0.240, 0.259] | 0.235 *** (0.005) [0.226, 0.245] | 0.293 *** (0.005) [0.283, 0.303] | 0.244 *** (0.005) [0.234, 0.253] |
Proportion of fixed assets | NO | NO | 0.397 *** (0.021) [0.356, 0.437] | 0.457 *** (0.022) [0.413, 0.500] | 0.354 *** (0.021) [0.314, 0.394] | 0.454 *** (0.022) [0.411, 0.498] |
Debt ceiling | NO | NO | 0.125 *** (0.017) [0.093, 0.158] | 0.118 *** (0.017) [0.084, 0.151] | 0.123 *** (0.016) [0.092, 0.153] | 0.117 *** (0.017) [0.084, 0.150] |
Holdings | NO | NO | YES | YES | YES | YES |
Industry sector | NO | NO | YES | YES | YES | YES |
Year fixed effects | NO | NO | NO | NO | YES | YES |
City fixed effect | NO | NO | NO | NO | YES | YES |
Constant term (math.) | 9.579 *** (0.010) [9.559, 9.599] | 8.835 *** (0.010) [8.815, 8.855] | 6.176 *** (0.064) [6.050, 6.302] | 6.181 *** (0.062) [6.059, 6.303] | 5.854 *** (0.064) [5.728, 5.980] | 6.120 *** (0.063) [5.996, 6.244] |
Adjusted R-squared | 0.002 | 0.002 | 0.165 | 0.140 | 0.172 | 0.141 |
Sample size | 294,957 | 283,524 | 259,596 | 250,348 | 259,596 | 250,348 |
Variable | Publicly Held | Publicly Held | Foreign Holding | Foreign Holding | Privately Held | Privately Held |
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
Did | −0.301 *** (0.048) [−0.395, −0.207] | −0.212 *** (0.045) [−0.301, −0.123] | −0.466 *** (0.084) [−0.631, −0.301] | −0.521 *** (0.073) [−0.663, −0.378] | −0.140 *** (0.029) [−0.198, −0.082] | −0.163 *** (0.029) [−0.220, −0.106] |
Year of business squared | −8.51 × 10−5 *** (1.48 × 10−5) [−1.14 × 10−4, −5.61 × 10−5] | −7.46 × 10−5 *** (1.49 × 10−5) [−1.038 × 10−4, −4.53 × 10−5] | 8.73 × 10−5 (9.47 × 10−5) [−9.83 × 10−5, 2.73 × 10−4] | 4.29 × 10−5 (1.075 × 10−4) [−1.679 × 10−4, 2.537 × 10−4] | −1.1 × 10−5 (2.01 × 10−5) [−5.04 × 10−5, 2.83 × 10−5] | −6.78 × 10−6 (2.14 × 10−5) [−4.87 × 10−5, 3.51 × 10−5] |
Total assets | 0.414 *** (0.010) [0.394, 0.434] | 0.356 *** (0.010) [0.338, 0.374] | 0.245 *** (0.017) [0.212, 0.278] | 0.284 *** (0.016) [0.252, 0.316] | 0.301 *** (0.006) [0.289, 0.313] | 0.258 *** (0.007) [0.245, 0.271] |
Proportion of fixed assets | 0.582 *** (0.046) [0.493, 0.673] | 0.656 *** (0.049) [0.560, 0.751] | 0.557 *** (0.067) [0.422, 0.692] | 0.688 *** (0.075) [0.541, 0.834] | 0.317 *** (0.029) [0.261, 0.373] | 0.449 *** (0.030) [0.390, 0.508] |
Debt ceiling | 0.064 *** (0.016) [0.033, 0.096] | 0.059 *** (0.017) [0.025, 0.093] | 0.247 *** (0.049) [0.151, 0.343] | 0.175 *** (0.053) [0.071, 0.279] | 3 × 10−5 (4.287 × 10−4) [−8.072 × 10−4, 8.732 × 10−4] | 0.207 *** (0.021) [0.165, 0.249] |
Holdings | YES | YES | YES | YES | YES | YES |
Industry sector | YES | YES | YES | YES | YES | YES |
Year-fixed effect | YES | YES | YES | YES | YES | YES |
City-fixed effect | YES | YES | YES | YES | YES | YES |
Adjusted R-squared | 0.297 | 0.256 | 0.098 | 0.100 | 0.115 | 0.083 |
Sample size | 68,952 | 66,030 | 23,451 | 23,227 | 120,299 | 118,165 |
Variable | Eastern Seaboard | National Average | ||
---|---|---|---|---|
(1) | (2) | (3) | (4) | |
Did | −0.286 *** (0.029) [−0.342, −0.230] | −0.077 *** (0.029) [−0.133, −0.021] | −0.119 *** (0.020) [−0.158, −0.080] | −0.051 *** (0.020) [−0.090, −0.013] |
Year of business squared | −2.55 × 10−5 (2.08 × 10−5) [−6.62 × 10−5, 1.52 × 10−5] | −1.55 × 10−5 (2.25 × 10−5) [−5.96 × 10−5, 2.86 × 10−5] | −3.88 × 10−5 *** (9.6 × 10−6) [−5.83 × 10−5, −1.93 × 10−5] | −3.46 × 10−5 *** (1.02 × 10−5) [−5.46 × 10−5, −1.45 × 10−5] |
Total assets | 0.274 *** (0.009) [0.257, 0.291] | 0.210 *** (0.009) [0.192, 0.227] | 0.298 *** (0.005) [0.289, 0.307] | 0.244 *** (0.005) [0.235, 0.253] |
Proportion of fixed assets | 0.350 *** (0.040) [0.271, 0.429] | 0.415 *** (0.044) [0.329, 0.500] | 0.315 *** (0.019) [0.278, 0.351] | 0.433 *** (0.021) [0.393, 0.474] |
Debt ceiling | 0.159 *** (0.028) [0.104, 0.214] | 0.113 *** (0.031) [0.053, 0.173] | 5.158 × 10−4 (8.796 × 10−4) [−1.2082 × 10−3, 2.2399 × 10−3] | 0.101 *** (0.014) [0.074, 0.127] |
Holdings | YES | YES | YES | YES |
Industry sector | YES | YES | YES | YES |
Year-fixed effect | YES | YES | YES | YES |
City-fixed effect | YES | YES | YES | YES |
Adjusted R-squared | 0.166 | 0.127 | 0.172 | 0.135 |
Sample size | 81,622 | 78,754 | 315,033 | 302,317 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Qian, J.; Han, X.; Ye, M.; Lv, Y.; Che, J. Environmental Benefits of the West-East Natural Gas Transmission Project: Cross-Regional Energy Optimization and Transformation for Sustainable Development. Energies 2024, 17, 3820. https://doi.org/10.3390/en17153820
Qian J, Han X, Ye M, Lv Y, Che J. Environmental Benefits of the West-East Natural Gas Transmission Project: Cross-Regional Energy Optimization and Transformation for Sustainable Development. Energies. 2024; 17(15):3820. https://doi.org/10.3390/en17153820
Chicago/Turabian StyleQian, Jiaorong, Xuze Han, Mao Ye, Yexin Lv, and Jing Che. 2024. "Environmental Benefits of the West-East Natural Gas Transmission Project: Cross-Regional Energy Optimization and Transformation for Sustainable Development" Energies 17, no. 15: 3820. https://doi.org/10.3390/en17153820
APA StyleQian, J., Han, X., Ye, M., Lv, Y., & Che, J. (2024). Environmental Benefits of the West-East Natural Gas Transmission Project: Cross-Regional Energy Optimization and Transformation for Sustainable Development. Energies, 17(15), 3820. https://doi.org/10.3390/en17153820