How to obtain industrial waste data at the county scale: Two downscaling models and their application in Dongguan, China

Abstract

The availability of environmental emission data is critical in evaluation of countries' ecological security and the implementation of environmental management. However, access to environmental emission data at the county level is not provided by statistical publications and bulletins. Therefore, in this paper, we develop two novel data downscaling models, an environmental Kuznets curve downscaling model (EKCDM) and a scale model (SM), to obtain county-level environmental emission data. The EKCDM relies on the EKC hypothesis and the assumption that the same model applies across scales, whereas the SM depends on the assumption that the share of a region's environmental pollution is equivalent to its share of economic output. Subsequently, environmental emission data above the county scale can be obtained through model transformation and simple calculations. By verifying and analyzing the official data with the one obtained through downscaling at municipal level and above, we verify the feasibility of the models, after which we apply the models to extrapolate information on the industrial waste of the counties of Dongguan city in Guangdong Province, China. We find that the EKCDM should be given priority in most cases, especially for the quadratic parameter model, and that the SM can be adopted when per capita gross domestic product differs greatly between adjacent levels of administrative units. In general, scholars can synthesize the characteristics of these two models, and obtain more accurate data by supplementing and verifying one with the other. Compared with other downscaling methods, our methods require far less data and the concepts are easily understood, which makes them more feasible and increases applicability. This study provides scholars with powerful tools to explore the relationship between industrial pollution and economic development in depth by obtaining industrial waste data at the county scale, thereby supporting scientific research and policy design.