Abstract

The tradeoff between economic growth and environmental protection has been a critical issue in facilitating eco-industrial park development in China. As the principal contributors to China's industrial output, many industrial parks have been addressing the issues of intensive resource consumption and pollutant generation, driven by much stricter regulations on the environment and resource management. Retuning the industrial structure is a substantial way to address the environmental issues while promoting economic development, which are the goals of eco-industrial development. This study proposes a multi-criteria industrial structure adjustment model by employing a generalized reduced gradient method to find the optimal structure of an industrial park. The model aims to increase the overall resource utilization efficiency and industrial output efficiency through a decoupling between the economic development and environmental burden of the park. A Chinese eco-industrial park located in the capital, the Beijing Economic-technological Development Area (BDA), is used as an example to uncover a transformation roadmap from a high-speed mode to a high-quality mode. The constraints of the multi-criteria decision-making model mainly focus on the limits of water consumption and pollutant emissions by targeting an appropriate economic development rate. The key findings are as follows. First, BDA could achieve 186% economic growth with 20% water consumption and 30% contaminant reduction in five years (2020–2025) by optimizing the industrial structure. Second, the advanced manufacturing industries play significant roles in stimulating the high-quality development. Third, ammonia nitrogen is a crucial factor restricting economic development under the requests of the “dual control” policy. Forth, the industry that can use reclaimed water in production will get more development opportunities and space, and vice versa. The model can be applied in diverse industrial parks by modifying the parameters and associated constraints.

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