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COLLEGE RANKINGS, LABOR MARKET OUTCOMES, AND ALUMNI
SATISFACTION
DJUMAYEVA GULNORA AMIRKULOVNA
Prepare: teacher of the “Special sciences” department of special school № 2 of
Chilonzor district of Tashkent city.
ANNOTATION:
China has historically imposed its green loan policy since 2012. We
examine the impact of green loan policy on the transformation of heavy polluters and find that
green loans are favorable towards their green mergers and acquisitions (M&As). Our results
also suggest that green loans provide better incentives to heavy polluters in regions of lower
pollution, with lower asset specificity, or with fewer financing constraints. Our additional test
suggests that the green loan policy may be meaningful for privately owned heavy polluters.
Good bank-firm relationships or less GDP pressure on local government may further strengthen
such incentives. This study may contribute to the discussion on green loans and the
transformation of heavy polluters with policy implications for emerging markets.
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,
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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. 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.
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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 1997). 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.
