Can collaborative innovation constrain ecological footprint?Empirical evidence from Guangdong-Hong Kong-Macao Greater Bay Area, China Abstract：Constraining ecological footprint (EF) has become an important policy priority to reach the carbon neutrality goal and deal with environmental issues, as it represents the socio-economic pressures on the environment. Current studies on the impact of collaborative innovation (CI) on EF is insufficient, although CI contributes to scientific productivity which benefits the technological progress in clean technology. In addition, seen from different perspectives, whether CI is effective in reducing EF has remain controversial. To further clarify this issue, this paper took Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China as a case, calculated EF between annual periods of 2008-2018 at the municipal level, measured CI from four dimensions and applied threshold regression models to compare the impact of CI on EF across different economic intervals. The findings showed that: the EF of GBA displayed an overall upward trend with prominent spatial heterogeneity during the study period. The impact of CI on EF presented a double-threshold effect when examined with different indicators. Among which, the flow of scientific personnel and capital boosted EF, which intensified with economic development; while collaboration in technology exerted significant inhibitory effects on EF, and the influence of inter-city knowledge collaboration was limited. This might inspire policymakers to adopt sustainable strategies depending on the type of CI and the economic status of the city to constrain the growth of EF, thus minimizing impacts of human activities on the environment.

In recent years, epidemics, earthquakes, forest fires, floods, and other public health emergencies and natural disasters have occurred frequently, and the role of emergency logistics has become more and more prominent. In this paper, on the basis of sorting out the current situation of emergency logistics guarantee in China, we use hierarchical analysis and fuzzy evaluation methods to calculate the weight of each index factor and guarantee the effectiveness of emergency logistics, respectively. The study gives examples to analyze and calculate the guarantee effectiveness of emergency logistics so as to draw the influence of emergency logistics. The key factors of emergency logistics can provide decision guidance for strengthening the construction of the emergency logistics system and improving the level of emergency logistics guarantee.

Constraining ecological footprint (EF) has become an important policy priority to reach the carbon neutrality goal and deal with environmental issues, as it represents the socio-economic pressures on the environment. Current studies on the impact of collaborative innovation (CI) on EF is insufficient, although CI contributes to scientific productivity which benefits the technological progress in clean technology. In addition, seen from different perspectives, whether CI is effective in reducing EF has remain controversial. To further clarify this issue, this paper took Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China as a case, calculated EF between annual periods of 2008-2018 at the municipal level, measured CI from four dimensions and applied threshold regression models to compare the impact of CI on EF across different economic intervals. The findings showed that: the EF of GBA displayed an overall upward trend with prominent spatial heterogeneity during the study period. The impact of CI on EF presented a double-threshold effect when examined with different indicators. Among which, the flow of scientific personnel and capital boosted EF, which intensified with economic development; while collaboration in technology exerted significant inhibitory effects on EF, and the influence of inter-city knowledge collaboration was limited. This might inspire policymakers to adopt sustainable strategies depending on the type of CI and the economic status of the city to constrain the growth of EF, thus minimizing impacts of human activities on the environment.