Green technology innovation is an important means to help reduce carbon emissions. Most of the current researches focus on the evaluation of green technology innovation and its relationship with economic factors, while ignoring its relationship with financial factors. In fact, financial development is an important driving force for further improving the efficiency of technological innovation, especially in developing countries. China, which has put forward the “double carbon” strategy (achieve peak carbon dioxide emission and carbon neutrality) in recent years, has a lot of interaction between green technology innovation and financial development, which is a good research sample. Based on the provincial panel data of 30 provinces in China from 2003 to 2020, this paper follows the research steps: 1) From the perspective of carbon emissions, we use the non-radial super-efficiency SBM-ML model to measure the total factor productivity of green technology innovation. 2) We analyze the temporal and spatial changes of green technology innovation and financial agglomeration in each province. 3) We establish a spatial simultaneous model of generalized three-stage least squares to study the intrinsic relationship between green technology innovation and financial agglomeration. The results show that: 1) The financial agglomeration level of each province is basically stable due to the fixedness of the financial core area, but the development speed of green technology innovation in the southeast coastal area is significantly higher than that in the inland area. 2) The interaction between green technology innovation and financial agglomeration has a nonlinear mechanism of “low-level inhibition and high-level promotion,” that is, low-level financial agglomeration has an inhibitory effect on green technology innovation, and as the level of financial agglomeration increases, its impact on green technology innovation gradually develops positively, and vice versa. 3) The green technology innovation and financial agglomeration in the surrounding provinces have positive and significant promoting effects on the green technology innovation and financial agglomeration in the province, but the impact of green technology innovation is significantly stronger than that of financial agglomeration. Finally, suggestions are put forward for further reducing carbon emissions, realizing the sustainable economic growth and approaching goals of “carbon peaking and carbon neutrality.”

Forest fires are a type of disaster with both human and natural factors; they differ from other forest disasters, in that they can cause significant damage not only to the ecological environments but also to the economy and society in many irreversible ways. While the risk factor of forest fires has been large, systematic studies on economic losses caused by forest fires have been lacking in recent years, and there is also a lack of analysis on forest fire economic losses in both spatial and temporal dimensions. Therefore, based on the forest fire data from 2006 to 2018, this paper establishes a forest fire economic loss evaluation system to calculate the economic losses in China and analyzes the spatial distribution characteristics and change trends of the forest fire economic losses in each province through thermal mapping. The results show the following. (1) The economic loss from forest fires in China is generally characterized by a fluctuating decline, but anomalous values due to human factors may occur. (2) The spatial heterogeneity of economic loss in China’s provinces is limited by many factors, such as the differences in resource endowments, showing the characteristics of “low in the eastern and western regions and high in the central region”. (3) Forest fires in China cause the most serious losses to forest ecological benefits. (4) Forest resources and fires are not independent of each other between regions, and areas with similar economic losses related to forest fires are often found in blocks. (5) Although the overall economic losses caused by forest fires in China are fluctuating and decreasing, some provinces are showing signs of increasing economic losses, most notably in Inner Mongolia. Therefore, this paper suggests targeted recommendations based on forest fires in different regions and with reference to the changing trends of economic loss caused by forest fires. For low-loss areas, we can further reduce the economic loss per unit area while ensuring that the losses do not increase any further. For high-loss areas, the main focus should be to find the weak points in the adaptation to forest fires. The right way to permanently reduce the damage caused by forest fires is to improve the adaptive and symbiotic capacity of the ecosystems and residential communities in relation to fires in a targeted manner and to improve the capacity for quick economic recovery after a fire.

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