Influencing factors and multi-scenario prediction of China's ecological footprint based on the STIRPAT model |
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| Institution: | 1. School of Culture and Tourism, Shanxi University of Finance and Economics, Taiyuan 030006, Shanxi, China;2. College of Resources and Environment, Shanxi Agricultural University, Taigu 030801, Shanxi, China;3. Department of Design Art, Taiyuan Institute of Technology, Taiyuan 030008, Shanxi, China;4. College of International Economics & Trade, Shanxi University of Finance and Economics, Taiyuan 030006, Shanxi, China;1. School of Land Science and Technology, China University of Geosciences, 29 Xueyuan Rd, Beijing 100083, China;2. Key Lab of Land Consolidation and Rehabilitation, Ministry of Land and Resources, 37 Guanying Rd, Beijing 100035, China;1. Shanghai Key Laboratory of Urbanization and Ecological Restoration, East China Normal University, Shanghai 200241, China;2. Shanghai Academy of Environmental Sciences, Shanghai 200233, China |
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| Abstract: | Focusing on the spatial and temporal pattern, evolution law, influencing factors and prediction of regional ecological footprint (EF) is conducive to promoting sustainable development of regional populations, resources, and the environment. Firstly, this study used the EF model to analyze the spatial and temporal patterns and dynamic evolution characteristics of the total ecological footprint (TEF) and the relative contributions of the biological ecological footprint (BEF), energy ecological footprint (EEF), and pollution ecological footprint (PEF) in China from 2000 to 2019. Secondly, the impact of socioeconomic factors on China's TEF was analyzed based on the STIRPAT model. Finally, the future development trend of China's TEF was analyzed by multi-scenario prediction. The results demonstrate that: (1) from 2000 to 2019, the TEF levels of China form three gradient spaces, (2) the BEF is the biggest contributor to EF in most of China's provinces, (3) total energy consumption is the most important positive factor for China's TEF while proportion of tertiary industry in the three industries is the most important negative factor, and (4) maintaining low growth in total energy consumption and high growth in proportion of tertiary industry in the three industries is crucial for limiting the future growth of China's TEF |
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