Assessing Chinese governance low-carbon economic peer effects in local government and under sustainable environmental regulation

Abstract

The current study developed a systematic analytical framework to explore the logic of forming the cohort effect of green governance and green development in China in the new era. Based on provincial panel data from 2008 to 2018, this paper examines the existence, scope, and induced control of the green governance peer effects using a spatial econometric approach. The study found that the following: (1) Influenced by the top-level design of the central government and the contradictory governance of regional development, the local governments form the peer effects in green governance activities. The existence of spatial relationships makes local governments dependent on a solid financial support system and a basis for industrial transformation, thus counteracting regional competition for green governance. (2) The green governance peer effects tend to decay with increasing geographical distance but do not disappear across regional boundaries under either spatial interaction framework. (3) Considering the impact of green governance policy systems and regional heterogeneity, the green governance peer effects decrease in the eastern, western, and central regions in that order. (4) Further, the influencing factors show that the green governance peer effects arise from intra-local government competition under the decentralization of power between the central and local governments. The competition for scales and the relative performance appraisal system reinforces the peer motivation of each subject. (5) The strong correlation of green governance willingness indicates that local governments cannot escape from will-led emotional behavior, and personal interests and governance motivation further drive the formation of pseudo-rational decisions, ultimately leading to irrational group decisions.

Keywords: Environmental foundations; Green governance; Incentive logic; Local government; Peer effects; Systematic analysis framework.