Environmental damage compensation for illegal solid waste dumping in China.

China aims to improve its system for pursuing environmental damage compensation environmental violation cases. To hold violators accountable, China has an effective system for the identification and assessment (I&A) of environmental damage. This study selected a typical case of the illegal dumping of solid waste (IDSW) in China to analyze the causes, the degree, and characteristics of environmental damage, focusing on components such as the physical quantification and valuation of damage. The findings were as follows: (1) Compensation claimants and obligors consider baseline damage confirmation and causality analysis key components of I&A. (2) The I&A process for a specific case needs to focus on key nodes such as the type, location, and duration of IDSW. (3) Restraining IDSW requires accurately quantifying the physical and value-related losses caused by solid waste dumping. (4) In the selected case study, the damage from environmental contamination caused by the IDSW incident amounted to 3938,990 yuan, including an environmental damage value of 3651,990 yuan and a transaction cost of 287,000 yuan. Both parties accepted the I&A calculation process in this case, and the desired punishment effect was achieved. Hence, the case study demonstrated that accurate I&A is the technical basis for environmental damage compensation. Thus, in the future, more attention should be paid to the role of scientific and technological means and knowledge reserves in the I&A of environmental damage.

The Premium of Public Perceived Greenery: A Framework Using Multiscale GWR and Deep Learning.

Population agglomeration and real estate development encroach on public green spaces, threatening human settlement equity and perceptual experience. Perceived greenery is a vital interface for residents to interact with the urban eco-environment. Nevertheless, the economic premiums and spatial scale of such greenery have not been fully studied because a comprehensive quantitative framework is difficult to obtain. Here, taking advantage of big geodata and deep learning to quantify public perceived greenery, we integrate a multiscale GWR (MGWR) and a hedonic price model (HPM) and propose an analytic framework to explore the premium of perceived greenery and its spatial pattern at the neighborhood scale. Our empirical study in Beijing demonstrated that (1) MGWR-based HPM can lead to good performance and increase understanding of the spatial premium effect of perceived greenery; (2) for every 1% increase in neighborhood-level perceived greenery, economic premiums increase by 4.1% (115,862 RMB) on average; and (3) the premium of perceived greenery is spatially imbalanced and linearly decreases with location, which is caused by Beijing's monocentric development pattern. Our framework provides analytical tools for measuring and mapping the capitalization of perceived greenery. Furthermore, the empirical results can provide positive implications for establishing equitable housing policies and livable neighborhoods.