Green loans and the transformation of heavy polluters: evidence from China

The People’s Bank of China (PBC), China Banking Regulatory Commission (CBRC), and State Environmental Protection (SEP) on 12 July 2007 jointly issued Notice on the Implementation of Environmental Protection Policies and Regulations to Prevent Credit Risks (the Notice). The Notice required the banks to use green loans as a market means for environmental protection, energy conservation, and emission reduction. However, the Notice did not provide any implementable details and remained mainly as a document.

Furthermore, the listed banks are mandated to disclose their green loan information to the public in their financial statements. The periodic reviews of green loans within the banks are also monitored by CBRC, which integrates the execution of green loan policy into its routine rating of banks, bank executive reviews, operation reviews, and evaluation of penalties.

On 3 November 2015, the Proposal of the CPC Central Committee on Formulating the 13th Five-year Plan for National Economic and Social Development was officially announced, where "developing green finance" became an important task of the supply-side structural reform.

On 31 August 2016, the People's Bank of China, the Ministry of Finance and the National Development and Reform Commission jointly issued the Guidance on Building a Green Financial System, stressing that developing green credit is the major component in the building China’s green financial system.

On 5 March 2017, Premier Keqiang Li stated in government work report that "vigorously developing green finance" is one of the important tasks to deepen the reform of the financial system.

The CSRC codes matching polluting industries cover B06, B07, B08, B09, C17, C19, C22, C25, C26, C28, C29, C30, C31, C32, D44. Firms in these industries are identified as heavy polluters.

M&A covers equity and asset acquisition and mergers. To avoid the impact of Chinese listed companies’ implementation of the ‘New Enterprise Accounting Standards’ from 2007 on key variables, M&As before 2008 are not considered. We exclude acquirers when their targets are non-Chinese firms. We also exclude Special Treatment (ST) firms or firms without complete financial data. We exclude M&As for asset divestiture or replacement, debt restructure, or share repurchase. We only keep equity acquisitions and exclude asset acquisitions such as those of land. We also exclude M&A transactions with a value of less than 1 million RMB or equity acquisitions of less than 30%. If the sample firm had multiple M&As with the same target in the same year, we merge the transactions. If the sample firm had multiple M&As with different targets, only the transaction of the largest value with highest ratio of equity is counted. Our sample selection criteria are mature. Given small-valued M&As have little impact on the market value of acquirers (Chatterjee and Lubatkin 1990), acquisition value less than 1 million RMB and less than 30% equity purchased are often excluded (Conn et al. 2004; Li et al. 2020a; Li et al. 2020b; Li et al. 2022a). In addition, M&As taking the form of debt restructuring and share repurchase do not allow the acquirer to choose the nature of their acquisitions and are subsequently excluded. To avoid repeated observations of the same acquirer of the same target firm, multiple M&As with the same target firm in the same year are merged. When the acquirer makes M&As with different targets, only the acquisition of the largest value and highest equity ratio is counted for the year.

In this process, besides the selection criteria in footnote 6, we exclude acquirer firms from emerging industries, which are unlikely to need strategic transformation. To ensure the accuracy of the matching, we select all possible control variables from the perspectives of firm characteristics, transaction features, and corporate governance as covariates for one-to-one nearest-neighbor matching. The covariates include all the control variables in our regression model, which are firm size (Size), debt ratio (Lev), return on equity (ROE), ownership type (Private), R&D (Research), and growth (Growth). Transaction characteristics such as transaction price (Price), acquisition ratio (Ratio), payment by cash or other (Cash), and related transactions (Related), In terms of corporate governance, duality (Dual), and board independence (Indep).

We winsorize to remove extreme values in our data. All continuous variables are winsorized at the 1% and 99% levels to mitigate the influence of outliers on the model estimation.

Green M&As may help optimize resource allocation, enhance the competitive advantage of firms, and realize economic as well as ecological benefits by effectively reducing environmental costs. Although there can be alternative means for polluters to transform green, green M&As are the most feasible and effective ones. A simple and direct reduction of pollution can be end-of-pipe treatment. Given the tangible pressure on heavy polluters, they may temporarily resort to this approach. However, fundamental changes towards green business structure and production, or front-end treatment, to reduce pollution and energy as well as improve resource efficiency, often require environmental investment and technological innovation (Hart and Ahuja 1996). Green loans are positive toward the front-end approach and promote technological innovation (Li et al. 2018; Wu and Xu 2022). Since corporate innovation is featured by long-term investment and highly risky outcomes, green M&As allow heavy polluters to timely acquire clean resources and technology for green transformation. In addition, green M&As enhance investment efficiency, scale efficiency, productivity, and green innovation of heavy polluters. Therefore, green M&A is one of the few effective means for heavy polluters to achieve green transformation and legitimacy (Li et al. 2020a).

Traditionally, profit and tax are regarded as an SOE’s contribution to the economy in China.