Regional carbon efficiency and corporate cash holdings: evidence from China

Climate change disrupts ecosystems and damages economic development and human health. China has ratified and implemented major international climate agreements (UNFCCC, Kyoto Protocol, Paris Agreement) and built a 1+N policy system to achieve peak carbon and carbon neutrality, revising and enacting multiple environmental and energy laws. Enterprises inevitably emit carbon during production. Maximizing economic output while minimizing emissions implies higher GDP per unit of carbon (regional carbon efficiency). Higher regional carbon efficiency suggests governments can better balance growth and emissions reduction, lowering policy pressure and reducing carbon risk imposed on firms. Meanwhile, corporate cash holdings have risen globally; excessive cash entails opportunity, management, and agency costs, particularly in settings with weaker governance like China. This study asks whether and how regional carbon efficiency affects firms’ cash holdings. The qualitative hypothesis is that higher regional carbon efficiency reduces firms’ carbon risk, stabilizes cash flows, eases financing constraints, and weakens precautionary motives, thereby lowering cash holdings. Moreover, by promoting long-term investment (a destination of cash) and enhancing debt financing capacity (a source of cash), regional carbon efficiency may further reduce cash. Empirically, the study uses a two-way firm and year fixed effects model and mediation tests to evaluate these mechanisms on Chinese A-share firms (2008–2019), contributing to the cash holdings literature and to research on the microeconomic consequences of climate-related policies and regional carbon efficiency.

The review covers three strands: (1) Factors influencing corporate cash holdings: Building on Keynes’ precautionary, transaction, and investment motives, recent work shows that uncertainty, governance, credit lines, innovation, financial constraints, corruption, green credit policy, and political/economic uncertainty shape cash policies. Cash can substitute/complement credit lines; innovation and market competition influence cash via financing frictions; green credit policies may raise cash by tightening loans; governance and uncertainty matter. (2) Economic changes in regional carbon efficiency: Lower regional carbon efficiency raises uncertainty about local carbon regulations, increasing firms’ carbon risk (regulatory, physical, business), thereby raising costs (taxes, compliance, litigation), affecting leverage, elevating financing constraints (loan terms, pricing), and altering corporate policies (M&A mix, risk, credit risk). Firms may hold more cash as a buffer against these risks. (3) Economic impacts of climate change: Climate change affects macroeconomic growth and productivity, with temperature increases depressing output and productivity; households adapt via higher energy usage. Banks are aware of climate impacts but cautious in implementation. Overall, prior work links carbon risk and climate factors to corporate outcomes but has seldom examined the direct relationship between regional carbon efficiency and firms’ cash holdings.

Data and sample: The study uses Chinese A-share listed firms from 2008–2019. Exclusions: ST and *ST firms, financial firms, missing data, extreme EBIT (> average total assets), extreme working capital/net assets (|>20), and firms with fewer than two observations. Final sample: 26,041 firm-year observations; continuous variables winsorized at 1% tails. Regional carbon emissions data are from CEADS; city-level statistics from China Urban Statistical Yearbook; other firm/region data from the National Bureau of Statistics and Wind; internal control index from DIB. City/province emissions for 2018–2019 were linearly interpolated (removed in a robustness test). Variables: - Dependent (cash holdings): Cash1 = cash and cash equivalents / total assets; Cash2 = (cash + trading financial assets) / (total assets − cash and cash equivalents). For robustness, rCash1 and rCash2 subtract industry averages. - Key independent variable (regional carbon efficiency): Ceff = city real GDP / city carbon emissions; robustness uses provincial rCeff = provincial real GDP / provincial carbon emissions. - Mediators: Debt (debt financing capacity) = (short-term loans + long-term loans + notes payable) / total assets; Linv (long-term investment) = cash paid for purchase of fixed assets, intangible assets, and other long-term assets / total assets. - Controls: TobinQ; Size (ln total assets); Age (ln(current year − establishment year + 1)); Soe (state ownership indicator); First (largest shareholder ownership share); Growth (sales growth); Roa; Tatr (asset turnover); Cf (operating cash flow / non-cash assets); Nwc (net working capital / net assets); Di (cash paid for dividends and interest / total assets). Additional regional controls in mediator models: InGDP (ln per capita real GDP, base 2008), Fsize (city loan balance / regional GDP), Pfdi (FDI/GDP). Models: - Baseline impact model: two-way firm and year fixed effects panel regressions of Cash on Ceff with controls and clustered SEs (firm and year). - Mechanism (mediation) tests: Three-step mediation following Wen & Ye (2014), estimating (i) Cash on Ceff; (ii) mediator (Debt or Linv) on Ceff; (iii) Cash on Ceff and mediator, all with firm and year FE and appropriate controls. - Endogeneity and instruments: Instrument for Ceff uses the same-year mean carbon efficiency of other prefecture-level cities (ivCeff). For Eq. (4) where mediator may be endogenous (bidirectional with cash), also use average mediator of other firms (ivDebt or ivLinv) alongside ivCeff. Weak IV assessed by Cragg-Donald Wald F. Robustness: (i) double-clustered SEs; (ii) alternative cash measures (rCash1, rCash2); (iii) alternative regional efficiency (rCeff at provincial level); (iv) alternative FE structures including region and industry FE; (v) lagged controls; (vi) excluding interpolated 2018–2019 emissions data.

• Sample and descriptive statistics: 26,041 firm-year observations (3,030 firms). Mean Cash1 = 0.1709; Cash2 = 0.2995; mean Ceff = 1.4663; mean Debt = 0.1751; mean Linv = 0.0520. - Baseline impact of regional carbon efficiency: In firm and year FE regressions with full controls, Ceff is significantly negative: Cash1 coefficient −0.0069 (SE 0.0012, p<0.01); Cash2 coefficient −0.0356 (SE 0.0055, p<0.01) (Table 2, cols. 3–4). Without controls, coefficients are also negative and significant (Cash1: −0.0477; Cash2: −0.1349; both p<0.01). - IV estimates for endogeneity: Using ivCeff (mean Ceff of other cities), Ceff remains negative and significant: Cash1 −0.0038 (SE 0.0020, p<0.10); Cash2 −0.0221 (SE 0.0062, p<0.01); with one-period lagged controls: Cash1 −0.0043 (SE 0.0019, p<0.05); Cash2 −0.0154 (SE 0.0050, p<0.01). Cragg-Donald Wald F statistics ≈ 15,000–16,000, far exceeding the 10% weak IV critical value (16.38), supporting instrument strength (Table 3). - Robustness: • Alternative cash measures (rCash1, rCash2): Ceff negative and significant (rCash1: −0.0051, SE 0.0012, p<0.01; rCash2: −0.0140, SE 0.0043, p<0.01). • Alternative regional efficiency (provincial rCeff): coefficients negative and significant (Cash1: −0.0954, SE 0.0178, p<0.01; Cash2: −0.4396, SE 0.0827, p<0.01) (Table 4A). • Alternative FE (region, industry, year): negative and significant across specifications (e.g., Cash2: −0.0094; Cash1: −0.0439; Cash2 with excluding interpolated years: −0.0056; Cash1: −0.0523; all p<0.01) (Table 4B). • Excluding interpolated 2018–2019 data leaves results intact. - Mechanism: Debt financing channel (Table 5): • Step 1 (Cash1): Ceff −0.0069 (p<0.01). • Step 2 (Debt): Ceff 0.0066 (SE 0.0012, p<0.01). • Step 3 (Cash1 on Ceff and Debt): Debt −0.1378 (SE 0.0073, p<0.01); Ceff −0.0063 (SE 0.0012, p<0.01) → partial mediation. • IV mediation addressing endogeneity: Step 2 (IV): Ceff 0.0056 (SE 0.0018, p<0.01); Step 3 (IV with ivDebt & ivCeff): Debt −0.1329 (SE 0.0080, p<0.01); Ceff −0.0037 (SE 0.0019, p<0.05). • Using Cash2 yields consistent results (Debt effect on Cash2 −0.0871, p<0.05; IV −0.0679, p<0.10; Ceff remains negative and significant). Interpretation: higher regional carbon efficiency improves debt financing availability, which lowers cash holdings. - Mechanism: Long-term investment channel (Table 6): • Step 1 (Cash1): Ceff −0.0069 (p<0.01). • Step 2 (Linv): Ceff 0.0024 (SE 0.0005, p<0.01). • Step 3 (Cash1 on Ceff and Linv): Linv −0.2234 (SE 0.0140, p<0.01); Ceff −0.0063 (SE 0.0012, p<0.01) → partial mediation. • IV mediation: Step 2 (IV): Ceff 0.0020 (SE 0.0007, p<0.01); Step 3 (IV with ivLinv & ivCeff): Linv −0.2288 (SE 0.0156, p<0.01); Ceff −0.0033 (SE 0.0020, p<0.10). • Using Cash2: Linv −1.0497 (SE 0.0510, p<0.01); IV −1.0584 (SE 0.0556, p<0.01); Ceff remains negative and significant (e.g., −0.0199, SE 0.0061, p<0.01). Interpretation: higher regional carbon efficiency stimulates long-term investment, which consumes cash and lowers cash holdings. Overall finding: Across specifications, instruments, and robustness checks, higher regional carbon efficiency significantly reduces firms’ cash holdings, operating through improved debt financing capacity and increased long-term investment.

Lower regional carbon efficiency heightens uncertainty about government carbon regulations, increasing firms’ carbon risk and associated costs (potential taxes, compliance, litigation), destabilizing energy supply and prices, and raising cash flow volatility. Lenders internalize carbon risk, tightening credit terms and raising financing costs, which increases firms’ precautionary cash demand. Improving regional carbon efficiency reduces these uncertainties, stabilizes cash flows, eases financing constraints, and weakens precautionary motives to hold cash, lowering cash balances. Mechanistically, higher regional carbon efficiency improves banks’ willingness to lend (creditors incorporate lower carbon risk into decisions), boosting firms’ debt financing capacity and reducing the need for liquidity buffers. Simultaneously, reduced uncertainty lowers the option value of waiting, encouraging irreversible long-term investments that consume cash. Together, these channels explain how regional carbon efficiency translates into lower corporate cash holdings and align with prior literature on precautionary cash, credit constraints, and investment under uncertainty.

Using Chinese A-share firms from 2008–2019, the study shows that higher regional carbon efficiency reduces corporate cash holdings. The effect operates through two channels: (1) improving firms’ debt financing capacity (alleviating financing constraints and lowering the need for cash buffers) and (2) promoting long-term investment (a cash-consuming use). Results are robust to alternative measures, fixed effects specifications, IV strategies, lagged controls, and excluding interpolated emissions data. Implications: Enhancing regional carbon efficiency can simultaneously support climate goals and firm development by easing financing frictions and reallocating cash toward investment. The findings suggest that encouraging carbon emissions reduction can be compatible with economic growth in developing countries, strengthening firms’ incentives to participate in peak carbon and carbon neutrality initiatives.

• Measurement of regional carbon efficiency: Defined as GDP per unit of carbon emissions, which is simple but potentially one-sided; CO2 is neither the only input nor the only undesirable output, and the metric focuses on economic outputs. Future work could develop more comprehensive total-factor carbon efficiency indicators. - Scope of determinants: The analysis emphasizes precautionary motives stemming from carbon risk. Corporate cash holdings are also shaped by internal governance, external financial development, and policy environments (e.g., agency issues, bank/market development, government quality, tax policies). Future research could examine heterogeneity across different governance, financial, and policy contexts.