Can digital finance reduce carbon emission intensity? A perspective based on factor allocation distortions: evidence from Chinese cities

Abstract

The world is facing the challenges of climate change and energy structure adjustments. The role of digital finance, a new branch of business that combines digital technology and traditional financial products, in reducing global carbon emissions needs to be studied. This paper uses panel data on 280 cities in China from 2011 to 2019 to empirically examine the efficacy of digital finance for governing carbon emission reductions and the mechanism by which it does so. The results show that (1) digital finance can facilitate carbon emission reductions and help reduce carbon emission intensity within regions; (2) digital finance helps promote the rational allocation of resources and alleviates factor distortions by encouraging firms to rationally use their own factor endowments so as to reduce carbon emission intensity, which holds robustly after considering the endogenous issues such as possibly omitting variables and collinearity; and (3) differences in geographical location, the vitality of regional innovation and entrepreneurship, regional willingness to protect the environment, and environmental protection levels lead to heterogeneity in the effect of digital finance on carbon emission intensity. Therefore, it is necessary to vigorously develop digital finance as a long-term tool for carbon governance.

Appendix

Following Kong et al. (2021) and Qiao et al. (2021), this paper first calculates the capital mismatch index ${Dis}_{Ki}$ and the labor mismatch index ${Dis}_{Li}$, which express the level of distortion in the factor market.

The details are as follows:

$$\begin{array}{cc}{Cmd}_{Ki}=\frac{1}{1+{Dis}_{Ki}},& {Lmd}_{Li}=\frac{1}{1+{Dis}_{Li}}\end{array}$$

(11)

where ${Cmd}_{Ki}$ and ${Lmd}_{Li}$ are the absolute distortion coefficients for the factor prices and indicate the addition of resources when there is relatively little distortion. In the actual calculation, the relative price distortion coefficient can be used instead:

$$\begin{array}{cc}Cmd_{Ki}^{'}=(\frac{Ki}K)/(\frac{S_i\beta_{Ki}}{\beta_K}),\;\;Lmd_{Ki}^{'}\end{array}=(\frac{Li}L)/(\frac{S_i\beta_{Li}}{\beta_L})$$

(12)

where ${s}_{i}=\frac{{p}_{i}{y}_{i}}{Y}$ indicates region$i$’s share ${y}_{i}$, ${\beta }_{K}={\sum }_{i}^{N}{s}_{i}{\beta }_{Ki}$ represents the output of the economy as a whole, and $Y$ represents the output-weighted contribution of capital. $\frac{{K}_{i}}{K}$ represents the actual ratio of the capital used by region $i$ to the total capital but $\frac{{s}_{i}{\beta }_{Ki}}{{\beta }_{K}}$ is the theoretical ratio of the capital used by region $i$ when capital is allocated efficiently. The ratio between the two reflects the degree of the deviation between the actual amount of capital used and the efficient allocation, that is, the degree of the capital mismatch in region$i$. If the ratio is greater than 1, then the cost of capital in region $i$ is low relative to that in the entire economy, leading an excessive amount of capital to be allocated to the region; in contrast, if the ratio is less than 1, it means that the actual allocation of capital in the region is lower than the efficient allocation. Relative to the theoretically optimal level, the amount of capital allocated is insufficient. As policymakers and implementers, local government officials, both politicians and economists, to achieve specific economic development goals, intervene in the capital market, affecting the normal allocations in the factor market and generating capital mismatches.

Next, a Cobb–Douglas production function is used to measure the output elasticity of capital ${\beta }_{K}$ and that of labor ${\beta }_{L}$. The specific form for the production function is as follows:

$${Y}_{it}={A}_{it}{K}_{it}^{{\beta }_{Kit}}{L}_{it}^{{\beta }_{Lit}}{E}_{it}^{{\beta }_{Eit}}.$$

(13)

Taking the natural logarithm of both sides at the same time, we can obtain

$$ln\;{A}_{it}=ln\;{Y}_{it}-{\beta }_{Kit}ln\;{K}_{it}-{\beta }_{Lit}ln\;{L}_{it}-{\beta }_{Eit}ln\;{E}_{it}$$

(14)

The whole sample of panel data can be analyzed in a pooled regression, or it can be analyzed by year or by city in order to select the best regression equation for estimating the sum of ${\beta }_{Kit}$ and ${\beta }_{Lit}$. Since the production function for each city is the same over time, estimating the regression by city is more in line with reality. Therefore, the regression is estimated by city with the linear prediction (LP) estimation method. After estimating the factor output elasticity for each province, we calculate the capital mismatch index ${Dis}_{Ki}$ and the labor mismatch index ${Dis}_{Li}$ for each city according to formulas (11) and (12).

The output variable (${Y}_{it}$). Output is expressed as the GDP of each city. Setting 2003 as the base period, the GDP of the other years is converted into real GDP in constant 2003 prices with the GDP deflator.

Labor input (${L}_{it}$). This study uses annual average employment in each city, that is, the arithmetic mean of employment at the beginning of the year (or, equivalently, employment at the end of the previous year) and employment at the end of the current year.

Capital investment (${K}_{it}$). Capital investment is expressed as the fixed capital stock in each city. Following Berlemann and Wesselhöft (2014)and Du and Lin (2015), it is calculated using the perpetual inventory method; the formula is as follows:

$${K}_{t}={I}_{t}/{P}_{t}+\left(1-{\vartheta }_{t}\right){K}_{t-1}$$

(15)

where ${K}_{t}$ is the current stock of fixed capital, ${I}_{t}$ is the current total flow of fixed capital in nominal terms, ${P}_{t}$ is the price index for investment in fixed assets, ${\vartheta }_{t}$ indicates the depreciation rate, and ${K}_{t-1}$ represents the stock of fixed capital in the previous period.

The energy input (${E}_{it}$). Since the regional energy input and carbon dioxide emissions are directly proportional, this paper uses the carbon dioxide emissions of each city to represent the input of energy.

Finally, the factor market distortions are calculated as follows:

$$DIS={{Dis}_{Kit}}^{\frac{{\beta }_{Kit}}{{\beta }_{Kit}+{\beta }_{Lit}}}{{Dis}_{Lit}}^{\frac{{\beta }_{Lit}}{{\beta }_{Lit}+{\beta }_{Kit}}}$$

(16)

Since there are two possible types of distortion, the underallocation of resources ($Dis$ > 0) and the overallocation of resources ($Dis$ < 0), to ensure a consistent direction for the regression estimates, this paper follows the practice of Shuhan et al. (2016) and utilizes the absolute value of ${Dis}_{Ki}$ and ${Dis}_{Li}$ so that the overall factor market distortion $DIS$ is positive. The larger the value of DIS is, the more severe the resource allocation.