The Impact of Digital Finance in G20 Economies on Environmental Protection, Energy Transition Policy, and Technological Innovation

Introduction

Objectives of the Study

• To examine the impact of digital finance on environmental protection in G20 economies.
• To assess how digital finance contributes to energy transition policies and sustainable technological innovation.
• To identify key economic and policy factors that influence the relationship between digital finance and sustainability.

Research Question and Hypotheses

Literature Review

Data and Method

Selections of Variables and Their Definitions

Control Variables

The process of selecting control variables, particularly at the local level, is the main focus of this work. Openness, often known as skill honesty, is measured as the percentage of total imports and exports to the GDP of each province. The average default rate of commercial bank institutions within each province is used to calculate the commercial bank default rate (Default). This acts as a stand-in variable. Targets for reducing emissions, adoption of Technological Innovation (Ben Amar, 2021),climate finance, and adaptation plans. Delegates from all across the world gather to discuss and work together on ways to address climate change, with scientific evidence highlighting the need for immediate and drastic action. The fixed asset investment in each province is measured by the adjusted proportion of fixed asset investment (FIX_ratio). An increase in capital investment in fixed assets can improve a region's appeal to capital. The logarithmic form of the corresponding scale represents the yearly foreign direct investment (FDI) in each province. The ratio of value added in the third industry to value added in the second industry in each province serves as the indicator of the economic structure (Eco Structure). An increase in this ratio frequently denotes a shift in the economic landscape, which fosters innovation, the creation of jobs, and adjustments in consumer behavior. The percentage of people in each province's labor force who have completed high school or more determines the education level indicator (Education). Communication, environmental preservation, and innovation are all positively impacted by higher education. The amount of money invested in research and development in each province determines the innovation indicator (R&D).

Mechanism Variables

Consumption volatility (Consume) was calculated using the HP filtering approach to isolate the trend term from provincial consumption expenditure data. The volatility term that resulted was then used to measure consumption volatility. Similarly, the same methodology was used to evaluate the volatility of green credit (Credit) using provincial green credit expenditure data. The volatility term's absolute value effectively captures the range of green credit variability and consumption swings.

Dependent Variable

The Super Slacks-Based Measure (SBM) model evaluates the Green Development Indicators (Green_L). This model includes unfavorable outputs and thoroughly analyzes economic green development. The following outlines the process for choosing particular methods and related indicators:

$$P^t\left(x^t\right)=\left\{\left(y^t,b^t\right)|{\stackrel{⃐}{x}}_{jm}^t\ge \sum _{j=1}^J{\lambda }_j^t{x}_{jm}^t,{\stackrel{⃐}{y}}_{jn}^t\le \sum _{j=1}^J{\lambda }_j^t{y}_{jn}^t,{\stackrel{⃐}{b}}_{jk}^t\ge \sum _{j=1}^J{\lambda }_j^t{b}_{jk}^t,{\lambda }_j^t\ge 0,\forall m,n,k\right\}$$

$$P^t\left(x^t\right)=\left\{\left(y^t,b^t\right)|{\stackrel{⃐}{x}}_{jm}^t\ge {\sum }_{j=1}^J{\lambda }_j^t{x}_{jm}^t,{\stackrel{⃐}{y}}_{jn}^t\le {\sum }_{j=1}^J{\lambda }_j^t{y}_{jn}^t,{\stackrel{⃐}{b}}_{jk}^t\ge {\sum }_{j=1}^J{\lambda }_j^t{b}_{jk}^t,{\lambda }_j^t\ge 0,\forall m,n,k\right\}$$

(1)

The super SBM model created by adding undesirable outputs to the model is as follows, and it is based on Tone's (2001) work:

$${\rho }^{*}=\mathrm{m}\mathrm{i}\mathrm{n}{\displaystyle \frac{\frac{1}{m}\sum _{i=1}^m\frac{{\stackrel{⃐}{x}}_i}{x_{i0}}}{\frac{1}{n+k}\left(\sum _{r=1}^n\frac{{\stackrel{⃐}{y}}_r}{y_{r0}}+\sum _{l=1}^k\frac{{\stackrel{⃐}{b}}_l}{b_{l0}}\right)}}$$

(2)

$$s.t.\{\begin{array}{c}\stackrel{⃐}{x}\ge \sum _{j=1,\ne 0}^J{\lambda }_j{x}_j,\\ \stackrel{⃐}{y}\le \sum _{j=1,\ne 0}^J{\lambda }_j{y}_j,\\ \stackrel{⃐}{b}\le \sum _{j=1,\ne 0}^J{\lambda }_j{b}_j,\\ \stackrel{⃐}{x}\ge x_0,\stackrel{⃐}{y}\le y_0,\stackrel{⃐}{b}\ge b_0，\stackrel{⃐}{y}\ge 0,{\lambda }_j\ge 0.\end{array}$$

Table 2. Provides a detailed explanation of the process used to select elements for the creation of green growth indicators.

Table 2. Provides a detailed explanation of the process used to select elements for the creation of green growth indicators.

Categories		Standard Metrics	Elements
Input elements		Employed population at year-end	Labour
		Capital stocks at year-end	Capital
		Total energy consumption at year-end	Energy
output elements	Real GDP at year-end		Economic output
	Industrial wastewater discharges Industrial exhaust emissions		Dewatering
			Emissions
			Solid wastes

Three sources provided the data used in this study: the "China Statistical Yearbook" (which covered the years 1998 to 2020), the "China Energy Statistical Yearbook," and the "China Environmental Statistical Yearbook." This study evaluates China's degree of green development between 1997 and 2019 using a sample of 30 provinces, cities, and autonomous areas from mainland China (Scrucca et al., 2023), excluding Tibet, to address the Tibetan region's data shortage. The eastern region has a higher index than the western and central regions. Furthermore, it is imperative to scrutinize if the impact of digital money on sustainable development differs based on specific components of sustainable development. This investigation will make it easier to formulate development policies that match accurately.

Results and Discussion

Discussion

Conclusion

References

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