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Collaborative innovation can promote scientific productivity and the development of clean technology and thus has a great potential in constraining the ecological footprint. However, current studies on the impact of collaborative innovation on ecological footprint are insufficient, and results remain controversial. To better understand these impacts, this paper took Guangdong-Hong Kong-Macao Greater Bay Area of China as a case, estimated the ecological footprint at the municipal level from 2008 to 2018, measured collaborative innovation both from four dimensions and from a composite approach, then applied threshold regression models to compare the impact of collaborative innovation on the ecological footprint across different economic intervals. The findings showed that: the ecological footprint of the Greater Bay Area displayed an overall upward trend with prominent spatial heterogeneity. The impact of collaborative innovation on the ecological footprint presented a double-threshold effect when examined with different indicators. Among which, the flow of scientific personnel and capital boosted the ecological footprint, which intensified with economic development, while collaboration in technology exerted significant inhibitory effects on ecological footprint, and the influence of inter-city knowledge collaboration was limited. Overall, collaborative innovation inhibited ecological footprint when measured by a composite index. This might inspire policymakers to adopt sustainable strategies depending on the type of collaborative innovation and the economic status of the city to constrain growth of the ecological footprint, thus minimizing the pressures of human activities on the environment and moving towards a more carbon neutral society.

Given the rapid development within China, the inequality of available water resources has been increasingly of interest. Current methods for assessing water stress are inadequate for province-scale rapid monitoring. A more responsive indicator at a finer scale is needed to understand the distribution of water stress in China. This paper selected Defense Meteorological Satellite Program Operational Line-scan System night-time stable lights as a proxy for water stress at the province level in China from 2004 to 2012, as night-time lights are closely linked with population density, electricity consumption and other social, economic and environmental indicators associated with water stress. The linear regression results showed the intensity of night-time lights can serve as a predictive tool to assess water stress across provinces with an R2 from 0.797 to 0.854. The model worked especially well in some regions, such as East China, North China and South West China. Nonetheless, confounding factors interfered with the predictive relationship, including population density, level of economic development, natural resource endowment and industrial structures, etc. The model was not greatly improved by building a multi-variable linear regression including agricultural and industrial indicators. A straightforward predictor of water stress using remotely sensed data was developed.