Borrowing-Firm Emission Trading, Bank Rate-Setting Behavior, and Carbon-Linked Lending under Capital Regulation
Abstract
:1. Introduction
2. Background
3. Model Setup and Solution
3.1. Conceptual Framework
3.2. Balance-Sheet Activities
3.3. Capped Barrier Call and Alternative Objectives
3.4. Solutions
4. Numerical Analysis
4.1. Data Description
4.2. Effect of Compensated Rate
4.3. Effect of Regulatory Cap Rate
4.4. Effect of Hedging
4.5. Effect of Capital-to-Deposits Ratio
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
Appendix A
Appendix B
- (1)
- The bank lending rate in China was 6.00% from September 1988 to May 2021; 12.24% in April 1996 was a record high, and 4.35% in May 2021 was a record low [35]. China’s banks increased to 273 () trillion in loans in March 2021, up from 136 () trillion in February and above market expectations of 245 () trillion [36]. Roughly speaking, we assume that the loan demand function faced by the bank was one specified as () = (4.25, 242), (4.50, 236), (4.75, 231), (5.00, 227), (5.25, 223), (5.50, 221), and (5.75, 220).
- (2)
- China’s green loan-to-total loan ratio was more than 10% during 2013~2020 [33]. We assume the high-emitter loan locus following = (4.25, 219), (4.50, 218), (4.75, 216), (5.00, 212), (5.25, 204), (5.50, 188), and (5.75, 156) where the amount of was approximately 90% of the number of total loans . Given a bundle of = (5.00, 204), we assume = 5.00 × 0.10 = 0.50 because the ratio of green loans to total loans equals 10%. Accordingly, we have the low-emitter loan locus as = (3.75, 22.0), (4.00, 22.1), (4.25, 22.3), (4.50, 22.6), (4.75, 23.0), (5.00, 23.5), and (5.25, 24.1).
- (3)
- The China bond interest rate was 2.52% on April 30, 2020, and 3.20% on the same date in 2021. We assume the liquid-asset interest rate of 2.86% () for our numerical analysis [37].
- (4)
- The debt-to-asset ratio of industrial Chinese state-owned enterprises peaked at 61.7%, in mid-2016, before sliding to 56.9% by the end of 2019 [38]. At an alternative point of = (4.35, 233), we assume 233/0.617 = asset = 377.63. Thus, we have = 377.63 × 0.9 = 339.87, and then = 377.63 − 339.87 = 37.76 due to Liu [33]. We take that the investment rate of return () equals 6.50%. We assume and are 10 and 1, respectively, to ensure that the firms’ capital-to-asset ratios are higher than the bank’s.
- (5)
- Narassimhan et al. [39] indicate that the cost of compliance (i.e., MRV costs) of the economic efficiency of the ETS regime in the European Union in 2016 was $72,440 per installation ($0.20 per tonne ), with two-thirds spent on monitoring. The administration cost was $2750 per installation. The stringency of cap (% cap reduction/year) was 2.20%. According to the indication, we assume = 2750/72,440 = 3.80%, = 2.20%, and = 0.60%. The rate assumptions about the carbon allowances imply a possible case of carbon neutrality.
- (6)
- The deposit rate range during 1990~2021 in China ranged from 0.35% to 3.15% [40]. We assume = (0.35% 3.15%)/2 = 1.75%. Tan and Floros [41] found that the equity-to-asset ratio in the China banks over 2003~2009 was 3.80%. We set the initial value of the capital-to-deposits ratio to 3.95% to ensure the equity-to-asset ratio was approximately 3.80%. To investigate the comparative statics, we fixed the liquid asset to 5 to calculate according to and Equation (3) in the numerical analysis. During 2009~2019, the mean empirical volatilities of the top-10-borrower and smaller-borrower loans were 0.426 and 0.496, respectively. Brockman and Turtle [20] report that asset volatilities display widely, a minimum of less than 0.0500 and a maximum above 3.4000. The mean asset volatility was 0.2904, with a standard deviation of 0.2608. Thus, we assume the asset volatility is 0.5000. We also believe that the swap hedging ratio is 0.25 initially and varies from 0.10 to 0.40.
- (7)
- Brockman and Turtle [20] found empirical evidence about the average barrier estimates by industry classification. For example, the average barrier was 0.7490 with a standard deviation of 0.1381 in the paper product industry, was 0.5632 with a standard deviation of 0.2567 in the chemical industry, and was 0.7777 with a standard deviation of 0.1607 in the petroleum industry. Generally speaking, the three industries are relatively heavy in carbon emissions. Thus, we assume that the barrier ratio is equal to 0.6966 ((0.7490 0.5632 0.7777)/3) for the numerical analysis.
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Variables | Approximation and Assumption |
---|---|
: high-emitter loan bundle | (4.25, 219) (4.50, 218) (4.75, 216) (5.00, 212) (5.25, 204) (5.50, 188) (5.75, 156) |
: low-emitter loan bundle | (3.75, 22.0) (4.00, 22.1) (4.25, 22.3) (4.50, 22.6) (4.75, 23.0) (5.00, 23.5) (5.25, 24.1) |
: liquid-asset rate | 2.86% |
: investment return rate | 6.50% |
: high-emitter equity capital | 10 |
: low-emitter equity capital | 1 |
: compensated rate | 0.50% |
: cap rate | 2.20% |
: the high-emitter marginal cost of the carbon allowances | 3.80% |
: the low-emitter marginal cost of the carbon allowances | 0.60% |
: deposit interest rate | 1.75% |
: barrier ratio | 69.66% |
: capital-to-deposits ratio | 3.95% |
: liquid-asset | 5 |
: instantaneous volatility | 0.50 |
: swap hedging ratio | 0.25 |
Scenario I | Scenario II | Scenario III | Scenario IV | |
---|---|---|---|---|
0.20 → 0.30 | 3128.6983 | 3312.6703 | 2345.4098 | 2168.5318 |
0.30 → 0.40 | 3127.9940 | 3311.1054 | 2345.4616 | 2168.7928 |
0.40 → 0.50 | 3127.2852 | 3309.5400 | 2345.5140 | 2169.0525 |
0.50 → 0.60 | 3126.5792 | 3307.9739 | 2345.5664 | 2169.3134 |
0.60 → 0.70 | 3125.8710 | 3306.4098 | 2345.6187 | 2169.5730 |
0.70 → 0.80 | 3125.1620 | 3304.8476 | 2345.6705 | 2169.8332 |
Scenario I | Scenario II | |
---|---|---|
1.90 → 2.00 | 2564.9932 | 1784.3066 |
2.00 → 2.10 | 2561.2297 | 1779.1733 |
2.10 → 2.20 | 2557.4436 | 1774.0563 |
2.20 → 2.30 | 2553.6386 | 1768.9528 |
2.30 → 2.40 | 2549.8110 | 1763.8706 |
2.40 → 2.50 | 2545.9644 | 1758.8005 |
Scenario I | Scenario II | Scenario III | Scenario IV | |
---|---|---|---|---|
0.10 → 0.15 | 85.6912 | −82.4970 | −55.3069 | −21.3134 |
0.15 → 0.20 | 103.6889 | −134.7135 | −58.1734 | −22.5496 |
0.20 → 0.25 | 77.9865 | −186.7910 | −61.3570 | −23.9485 |
0.25 → 0.30 | 20.1971 | −238.1676 | −64.9145 | −25.5467 |
0.30 → 0.35 | −59.7193 | −288.1573 | −68.9182 | −27.3938 |
0.35 → 0.40 | −151.6846 | −335.9474 | −73.4616 | −29.5584 |
Scenario I | Scenario II | Scenario III | Scenario IV | |
---|---|---|---|---|
(10−3) | ||||
3.65 → 3.75 | −21.2108 | −31.6794 | −2.9865 | −1.4868 |
3.75 → 3.85 | −21.0481 | −31.2066 | −2.9753 | −1.4891 |
3.85 → 3.95 | −20.8876 | −30.7473 | −2.9643 | −1.4913 |
3.95 → 4.05 | −20.7293 | −30.3010 | −2.9533 | −1.4935 |
4.05 → 4.15 | −20.5732 | −29.8671 | −2.9425 | −1.4957 |
4.15 → 4.25 | −20.4191 | −29.4451 | −2.9318 | −1.4979 |
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Chen, S.; Huang, F.-W.; Lin, J.-H. Borrowing-Firm Emission Trading, Bank Rate-Setting Behavior, and Carbon-Linked Lending under Capital Regulation. Sustainability 2022, 14, 6633. https://doi.org/10.3390/su14116633
Chen S, Huang F-W, Lin J-H. Borrowing-Firm Emission Trading, Bank Rate-Setting Behavior, and Carbon-Linked Lending under Capital Regulation. Sustainability. 2022; 14(11):6633. https://doi.org/10.3390/su14116633
Chicago/Turabian StyleChen, Shi, Fu-Wei Huang, and Jyh-Horng Lin. 2022. "Borrowing-Firm Emission Trading, Bank Rate-Setting Behavior, and Carbon-Linked Lending under Capital Regulation" Sustainability 14, no. 11: 6633. https://doi.org/10.3390/su14116633