Can Green Credit Policy Promote the High-Quality Development of China’s Heavily-Polluting Enterprises?

Abstract

Prior literature on the green innovation effects of green credit policies is extensive. However, few scholars have focused on the impact of green credit policies on the high-quality development of heavily-polluting enterprises. Based on this, this study employs the difference-in-differences (DID) model to explore the causal relationship between the Green Credit Guidelines (Guidelines) issued in 2012 and the high-quality development of heavily-polluting enterprises. Additionally, we test whether the effect of upgrading human resources in enterprises strengthens this causal relationship. Our findings suggest that the implementation of the Guidelines has significantly promoted the development quality of heavily-polluting enterprises and the promotion effect is more significant in enterprises with higher development quality, state-owned enterprises, large-scale enterprises, and enterprises in the western region of China. Further research reveals that the effect of upgrading human resources in enterprises has reinforced the positive impact of Guidelines on the high-quality development of enterprises. From the perspective of high-quality development of enterprises, in this paper, we expand the research into the effects of green credit policy, providing a decision-making reference for the promotion and improvement of subsequent green credit policy in the future.

How does China’s green credit policy affect the innovation of high-polluting enterprises? From the perspective of innovation quantity and quality

“…Otherwise, it takes on a value of 0. The compilation of heavy-polluting firms is founded on the revised Guidelines on Industry Classification of Listed Firms by the China Securities Regulatory Commission in 2012, along with the List of Listed Firms for Environmental Verification Industry Classification in 2008 [35,58,59]. This encompasses sectors such as thermal power, steel, cement, electrolytic aluminum, coal, metallurgy, building materials, mining, chemicals, petrochemicals, pharmaceuticals, light, textiles, and leather (codes: B06, B07, B08, B09, B11, C17, C18, C19, C22, C25, C26, C27, C28, C29, C30, C31, C32, C33, D44.).…”

“…To rigorously assess the policy's impact while minimizing the influence of external factors, the study draws on pertinent literature to employ a strategy of controlling a range of variables at both the enterprise and provincial levels [59,60]. At the enterprise level, referring to the existing literature [14], we control the following variables: Size of the enterprise(SIZE), liabilities of the enterprise(LIABILITY), Duration of enterprise existence(AGE), The ratio of assets to liabilities(LEVERAGE), Revenue from operations (OR), Profit after tax (NP), Cash ratio of assets (CFO), Net profit as a percentage of total assets (ROA), Tobin Q(TQ), government subsidies(SUBSIDY), the largest holder rate (HOLD).…”

“…This study categorizes enterprises into three distinct subgroups: Eastern, Central, and Western regions. This categorization is based on the varying economic development levels of their respective locations [59]. The Eastern region encompasses areas such as Beijing, Tianjin, Shanghai, Hebei, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, Hainan, Heilongjiang, Jilin, and Liaoning.…”