Spatio-temporal evolution and optimization analysis of ecosystem service value - A case study of coal resource-based city group in Shandong, China

Abstract

Coal-resource-based cities (CRBCs) have played an essential role in China's economic development over the past few decades, but ecological problems have become increasingly prominent. CRBCs are now facing a critical transformation node under the goal of carbon neutrality. Based on the balance of economy, ecology and energy, we put forward a method to evaluate and optimize sustainable urban development . In this study, we analyzed the land use/land cover change (LUCC) and ecosystem service value (ESV) of coal resource-based city group of Shandong province (CRBCGS) from 2000 to 2018 and the contribution of 16 natural and social production indicators to LUCC was assessed by using the PLUS model. We set up, optimized, and simulated three scenarios for 2030: Business as usual (BAU), Ecological conservation first (ECF), and Economic development first (EDF). The results showed that from 2000 to 2010, the LUCC of SDRBCG was violent, and 15.12% (7084.16 km 2 ) of the land has transformed, mainly caused by the circulation of construction land and farmland, and the land circulation rate decreased from 2010 to 2018. The ESV dropped sharply from 85677.42 million yuan in 2000–78050.77 million yuan in 2015 before rising slightly in 2018. In 18 years, ESV of 49.16% of the land decreased, while ESV of 11.46% increased. In 2030, the ESV of BAU, ECF, and EDF scenarios would be 77437.60, 79630.49, and 76894.29 million yuan. It can be seen that ESV has apparent differences under different policy-oriented. Finally, we identified the regions with significant ESV changes in 2030 by grid analysis. 16.63% of the ECF scenario's areas would show ecological improvement, significantly higher than BAU and EDF. The methods and results of this study can provide valuable data for the management to make urban environmental protection and green sustainable development planning. • Proposed a method of urban space optimization and ecological change prediction coupled with PLUS, MOP and grid analysis. • Analyzed the response of LUCC and ESV by using various visual methods and simulated three development scenarios in 2030. • Found the driving effect of different influencing factors on LUCC. • The optimized result meets the existing policies, fits the reality, and significantly improves the ESV.