Transport carbon emissions reduction efficiency and economic growth: a perspective from nighttime lights remote sensing

• Autorzy: Yan Yuxiang , Li Jianing

Identyfikatory

DOI

10.5604/01.3001.0016.312

• Języki publikacji: EN

Abstrakty

EN

The carbon emissions are essential for climate change and 26% of the world's carbon emissions are related to transport. But focusing only on fewer carbon emissions might be biased at times. In order to keep a balance between economic growth and carbon emissions reduction, this paper evaluated the performance of carbon control by considering the input factors and output factors together, which is more comprehensive and reliable. Firstly, this paper has calculated the transport carbon emissions reduction efficiency (TCERE) based on the model of super SBM with undesirable outputs. The input factors include capital stock, labor force and fossil energy consumption. And the output factors include gross domestic product and carbon dioxide emissions. Then the influencing factors of TCERE were analyzed using econometric models. The economic growth, transport structure, technology level and population density were posited as influencing factors. This paper creatively proposed the per capita nighttime lights brightness as a new indicator for economic growth. An empirical study was conducted in East China from 2013 to 2017, and this study has found that the relationship between TCERE and economic growth shows an U-shape. Besides, transport structure and technology level both show a positive impact on TCERE. The implications of our findings are that: (a) The TCERE declines slower in East China, giving us reason to believe that the improvement of TCERE is predictable; (b) When economic growth exceeds the turning point, economic growth is conducive to the improvement of TCERE. We could develop the economy boldly and confidently; (c) Increased investment in railway and waterway transportation infrastructure projects is needed to strengthen the structure of the railway and waterway transportation systems. Furthermore, the general public and businesses should be encouraged to prefer rail or river transportation; (d) Investment in scientific and technological innovation should be enhanced in order to produce more efficient energy-use methods.

Słowa kluczowe

EN

transport; carbon emissions reduction; control of emissions; data envelopment analysis; nighttime lights

PL

transport; redukcja emisji dwutlenku węgla; światła nocne; teledetekcja

• Wydawca: Warsaw University of Technology, Faculty of Transport
• Czasopismo: Archives of Transport
• Rocznik: 2023
• Tom: Vol. 66, iss. 2
• Strony: 21--32
• Opis fizyczny: Bibliogr. 30 poz., il., tab., wykr., wzory

Twórcy

autor

Yan Yuxiang

• Institute of Forestry Engineering, Guangxi Eco-engineering Vocational & Technical College, Liuzhou, China

• https://orcid.org/0000-0001-8796-7465

autor

Li Jianing

• Institute of Forestry Engineering, Guangxi Eco-engineering Vocational & Technical College, Liuzhou, China

• https://orcid.org/0000-0002-2995-3150

Bibliografia

• [1] An, C., Muhtar, P., Xiao, Z. (2022). Spatiotem poral Evolution of Tourism Eco-Efficiency in Major Tourist Cities in China. Sustainability, 14(20), 13158.
• [2] Blesl, M., Das, A., Fahl, U., Remme, U. (2007). Role of energy efficiency standards in reducing CO2 emissions in Germany: An assessment with TIMES. Energy Policy, 35(2), 772-785.
• [3] Chen, M.g., Zhang, S. (2021). Comparison of regional resident living standards across China: Based on the Global Night-time Lights Data. Economic Theory and Business Management, 41(5), 48-67.
• [4] Cheng, G. (2014). Data Envelopment Analysis: Methods and MaxDEA Software. Beijing: Intellectual Property Publishing House.
• [5] Chu, J.F., Wu, J., Song, M.L. (2018). An SBMDEA model with parallel computing design for environmental efficiency evaluation in the big data context: a transportation system application. Annals of Operations Research, 270, 105-124.
• [6] Churchill, S.A., Inekwe, J., Ivanovski, K., Smyth, R. (2018). The Environmental Kuznets Curve in the OECD: 1870-2014. Energy Economics, 75, 389-399.
• [7] Cooper, W.W., Seiford, L.M., Tone, K. (2007). Data Envelopment Analysis. New York: Springer.
• [8] Cui, Q., Li, Y. (2015). An empirical study on the influencing factors of transportation carbon efficiency: Evidences from fifteen countries. Applied Energy, 2015, 141, 209-217.
• [9] Cullinane, K., Wang, T.F., Song, D.W., Ji, P. (2006). The technical efficiency of container ports: Comparing data envelopment analysis and stochastic frontier analysis. Transportation Research Part A: Policy and Practice, 40(4), 354-374.
• [10] Fan, J., Ren, Y., Yu, X. (2017). An analysis of the Mechanism Between Population Urbanization and Regional Green Economic Efficiency Based on Stochastic Frontier Analysis Model. Journal of Macro-quality Research, 5(4), 52-65.
• [11] Gu, Y., Shao, Z., Huang, X., Cai, B. (2022). GDP Forecasting Model for China Provinces Using Nighttime Light Remote Sensing Data. Remote Sensing, 14(15), 3671.
• [12] Kang, Y. (2021). China Statistical Yearbook. Beijing: China Statistics Press.
• [13] Lan, Z., Zhang, H. (2014). Study on Inter-provincial Difference in Carbon Emissions Efficiency of Traffic and Transportation Industry in China. Logistics Technology, 33(4), 132-135.
• [14] Park, Y.S., Lim, S.H., Egilmez, G., Szmerekovsky, J. (2018). Environmental efficiency assessment of U.S. transport sector: A slack-based data envelopment analysis approach. Transportation Research Part D: Transport and Environment, 61, 152-164.
• [15] Qin, M., Liu, X., Li, S. (2019). The Impact of Urban Sprawl on Regional Economic Growth - Empirical Researches Based on DMSP Night-Time Light Data. China Economic Quarterly, 18(2), 527-550.
• [16] Tan, M., Li, X., Li, S., Xin, L., Wang, X., Li, Q., Li, W., Li, Y., Xiang, W. (2018). Modeling population density based on nighttime light images and land use data in China. Applied Geography, 90, 239-247.
• [17] Tian, G., Li, J., Miao, C., Du, P. (2022). Urban Green Development Efficiency and Its Influencing Factors in China Based on the Undesirable Outputs. Economic Geography, 42(6), 83-91.
• [18] Tsolas, I.E. (2022). Assessing Regional Entrepreneurship: A Bootstrapping Approach in Data Envelopment Analysis. Stats, 5(4), 1221-1230.
• [19] Wang, K., Wei, Y.M., Zhang, X. (2012). A comparative analysis of China's regional energy and emission performance: Which is the better way to deal with undesirable outputs. Energy Policy, 46, 574-584.
• [20] Wang, K.L., Miao, Z., Zhao, M.S., Miao, C.L., Wang, Q.W. (2019). China's provincial total-factor air pollution emission efficiency evaluation, dynamic evolution and influencing factors. Ecological Indicators, 107, 105578.
• [21] Wang, X., Wei, Q., Hu, X. (2016). Comprehensive Evaluation of Cities' Green Economy Efficiency and Spatial and Temporal Differentiation in China: Based on the DEA-BCC and Malmquist Model. Ecological Economy, 32(3), 40-45.
• [22] Wang, Z., Li, M., Jiang, J. (2020). The Impact of Traffic Accessibility on Urban Economic Growth-Spatial Econometric Analysis of DMSP/OLS Nighttime Satellite Lighting Data Based on 283 Cities. China Economic Studies, 9(5), 84-97.
• [23] Xu, K., Chen, F., Liu, X. (2015). The Truth of China Economic Growth: Evidence from Global Night-time Light Data. Economic Research Journal, 50(09), 17-29.
• [24] Xu, X., Zhao, T., Liu, N., Kang, J. (2014). Changes of energy-related GHG emissions in China: An empirical analysis from sectoral perspective. Applied Energy, 132, 298-307.
• [25] Yan, Y., Hu, W. (2020). Does Foreign Direct Investment Affect Tropospheric SO2 Emissions? A Spatial Analysis in Eastern China from 2011 to 2017. Sustainability, 12(7), 2878.
• [26] Zhang, C., Li, J., Liu, T., Xu, M., Wang, H., Li, X. (2022). The Spatiotemporal Evolution and Influencing Factors of the Chinese Cities Ecological Welfare Performance. International Journal of Environmental Research and Public Health, 19(19), 12955.
• [27] Zhao, P., Zeng, L., Lu, H., Zhou, Y., Hu, H., Wei, X. (2020). Green economic efficiency and its influencing factors in China from 2008 to 2017: Based on the super-SBM model with un desirable outputs and spatial Dubin model. Science of the Total Environment, 741, 140026.
• [28] Zhao, P., Zeng, L., Li, P., Lu, H., Hu, H., Li, C., Zheng, M., Li, H., Yu, Z., Yuan, D. (2022). China's transportation sector carbon dioxide emissions efficiency and its influencing factors based on the EBM DEA model with undesirable outputs and spatial Durbin model. Energy, 238, 121934.
• [29] Zhao, X., Wang, J., Xin Fu., Zheng, W., Li, X., Gao, C. (2022). Spatial-temporal characteristics and regional differences of the freight transport industry's carbon emission efficiency in China. Environmental Science and Pollution Research, 29, 75851-75869.
• [30] Zhang, Y., Shen, L., Shuai, C., Tan, Y., Ren, Y., Wu, Y. (2019). Is the low-carbon economy efficient in terms of sustainable development? A global perspective. Sustainable Development, 27(1), 130-152.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)