[1] Lai M D, Yong X, Shi W J. Explanation model of global warming: Greenhouse effect theory and natural fluctuation hypothesis of climate[J]. Studies in Dialectics of Nature, 38, 69-74(2022).

[2] Lin Z H. Frequent extreme weather "climate refugees" surge[N]. People's Daily Overseas Edition.

[3] Fan X, Qin Y Y, Gao X. Interpretation of the main conclusions and suggestions of IPCC AR6 working group I report[J]. Environmental Protection, 49, 44-48(2021).

[4] Kuang S Y, Zhou Z Y, Liang M C et al. Interpretation of the main conclusions of IPCC AR6 working group II report[J]. Environmental Protection, 50, 71-75(2022).

[5] Wang L, Zhang B C, Shi Y et al. Interpretation of the IPCC sixth assessment report on the impacts and risks of climate change[J/OL]. Climate Change Research Advances, 1-8(2022). http://kns.cnki.net/kcms/detail/11.5368.P.20220616.2032.004.html

[6] Qin Y, Xu X W, Wang L et al. Interpretation of the IPCC sixth assessment report on adaptation options of climate change[J/OL]. Climate Change Research, 1-9(2022). https://kns.cnki.net/kcms/detail/11.5368.P.20220707.1645.006.html

[7] Wu C X, Jiang C C. Climate change will seriously affect the survival of animal and plant species[J]. Grand View of Science, 20-23(2022).

[8] Chen F. Research on China's economic growth after the reform and opening-up[J]. Trade Show economy, 150-152(2022).

[10] LU D D. Medium growth: Sustainable development of China's economy[J]. Geographical Science, 35, 1207-1219(2015).

[11] Liu L Y, Bai Y, Sun R et al. Stereo observation and inversion of the key parameters of global carbon cycle:project overview and mid-term progresses[J]. Remote Sensing Technology and Application, 36, 11-24(2021).

[12] Zhang D Y, Zhang L X. Progress in estimation method of carbon emission[J]. Inner Mongolia Forestry Science and Technology, 20-23(2005).

[13] Hu Y L, Zhu Q Y. Analysis on the revision of waste volume of IPCC guidelines for national greenhouse gas inventory IPCC 2006 (revised edition in 2019)[J]. Low Carbon World, 11, 49-50(2021).

[14] Liu M D, Meng J J, Liu B H. Progress in the studies of carbon emission estimation[J]. Tropical Geography, 34, 248-258(2014).

[15] Doll C H, Muller J P, Elvidge C D et al. Night-time imagery as a tool for global mapping of socioeconomic parameters and greenhouse gas emissions[J]. AMBIO: A Journal of the Human Environment, 29, 157(2000).

[16] Ghosh T, Elvidge C D, Sutton P C et al. Creating a global grid of distributed fossil fuel CO2 emissions from nighttime satellite imagery[J]. Energies, 3, 1895-1913(2010).

[17] Ou J, Liu X, Li X et al. Mapping global fossil fuel combustion CO2 emissions at high resolution by integrating nightlight, population density, and traffic network data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 1674-1684(2016).

[18] Wang Z, Ye X. Re-examining environmental Kuznets curve for China's city-level carbon dioxide (CO2) emissions[J]. Spatial Statistics, 21, 377-389(2017).

[19] Su Y X, Chen X Z, Ye Y Y et al. The characteristics and mechanisms of carbon emissions from energy consumption in China using DMSP/OLS night light imageries[J]. Journal of Geographical, 68, 1513-1526(2013).

[20] Liu J J, Shi D, Wang C. A study on spatial spillover and correlation effect of carbon emissions across 30 provinces in China[J]. Journal of Natural Resources, 30, 1289-1303(2015).

[21] Chen C C, Cai B F, Sun F et al. Spatial agglomeration effects of carbon dioxide emissions between Beijing-Tianjin-Heibei region and Yangtze River Delta region[J]. China Environmental Science, 37, 4371-4379(2017).

[22] Jiang Y Y, Peng L, Liu L. Study on China's regional power carbon emission efficiency and spatial relation[J]. Journal of Hunan University of Finance and Economics, 35, 75-82(2019).

[23] Xia Y, Cai L H. Center displacement and spatial dependence of carbon emission in Hunan Province[J]. Sci-Tech Innovation and Productivity, 25-29(2020).

[24] Gong Q N, Wang Y Y, Tong Y F. Population's pressure on carbon emissions in Beijing-Tianjin-Hebei region: Spatial pattern and change analysis[J]. Journal of Capital University of Economics and Business, 22, 56-67(2020).

[25] Hu M R. Spatial Pattern and Influencing Factors of Carbon Emissions in China[D], 10-33(2021).

[26] Li J B, Huang X J, Wu C Y et al. Spatial pattern forecast of China's provincial carbom emissions[J]. Ecological Economy, 33, 46-52(2017).

[27] Shi Q Q, Lu F X, Chen H et al. Temporal-spatial patterns and factors affecting indirect carbon emissions from urban consumption in the Central Plains Economic Region[J]. Resources Science, 40, 1297-1306(2018).

[28] Qin Y, Yu R, Yu Z X et al. Spatial and temporal characteristics of land use carbon emission intensity in the central area of Yangtze River Delta from 2000 to 2018[J]. Journal of Henan Agricultural University, 55, 132-140(2021).

[29] Li Z K, Ren L Y, Ma R F et al. Analysis of spatial-temporal pattern and driving factors of carbon emissions in Zhejiang Province based on spatial-temporal geographic weighted regression model[J]. Journal of Ningbo University (Natural Science & Engineering Edition), 34, 105-113(2021).

[30] Zhao X C, Niu Y W, Xiao J et al. Spatial and temporal pattern of carbon emissions in Yueyang City based on land use change[J]. Journal of Hunan University of Technology, 36, 10-19(2022).

[31] Lei Y F, Lu C Y, Su Y et al. Research on the coupling relationship between urban vitality and urban sprawl based on the multi-source nighttime light data: A case study of the west Taiwan strait urban agglomeration[J]. Human Geography, 37, 119-131(2022).