Abstract
As a new economic form, the digital economy provides new opportunities to save energy and reduce emissions, but it may also become a booster for increasing energy consumption and pollutant emissions, making the sustainable development of cities face serious challenges. It is controversial whether the digital economy contributes to energy saving and emission reduction, and it is unclear what role technological progress and structural transformation play in the relationship between the digital economy and energy saving and emission reduction. In this study, the energy saving and emission reduction effects of the digital economy in cities and its mechanisms are empirically examined from the perspective of technological progress and structural transformation using the two-way fixed effect model and the mediating effect model. The results show that the digital economy increases urban energy consumption and reduces pollution emissions. The energy saving and emission reduction effects of the digital economy are more significant in northeastern cities, large cities, and resource-based cities. The mechanism test shows that the digital economy promotes urban energy saving and emission reduction through green technological progress, mainly because the energy saving from green technological efficiency improvements exceeds the energy rebound from green technological innovation. However, the digital economy has not shown energy saving and emission reduction effects by promoting industrial structural transformation. Specifically, the structural transformation of the secondary industry contributes to urban emission reduction, while the structural transformation of the primary and tertiary industries leads to efficiency loss of energy saving and emission reduction. Our findings provide valuable insights into the synergistic governance of “energy saving” and “emissions reduction” in cities in the digital economy.
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Data availability
The datasets used and/or analyzed during the current study are available on reasonable request.
Notes
The industrial structure change index, which is exogenous to technological progress, captures changes in the industrial structure and enables accurate identification of the impacts of technological progress and industrial structural transformation on energy consumption and environmental pollution. See Shen et al. (2021) for a detailed discussion.
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This work was supported by China Scholarship Council “National Program for Building Highly Qualified Universities” (Grant No. 201906170099; Grant No. 202006170081).
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Appendix
Appendix
As a complement to the theoretical analysis of this paper, a technical mathematical derivation of the technological effect and structural effect of the digital economy is presented in the Appendix.
Production function and decision assumptions
The primary product sector has a constant elasticity of substitution (CES) production technology and uses secondary products from n industries as intermediate inputs.
\({\Omega }_{t}\) denotes the number of primary products produced in the primary products sector; \({\Theta }_{it}\) denotes the number of secondary products produced in the secondary product sector; \({\varphi }_{i}\) denotes the importance of the secondary product inputs to the production of primary products, where \({\varphi }_{i}>0\) and \(\sum {\varphi }_{i}=1\); and \(\rho\) denotes the substitution elasticity of the secondary product inputs, where \(\rho >0\).
Given price \({p}_{t}\) for the primary product sector and price \({p}_{it}\) for the secondary product i, producers in the primary product sector choose the optimal input of secondary products \({\Theta }_{it}\) to maximize profit.
There are n industries in the secondary product sector, each of which produces its secondary products by inputting labor and tertiary products produced by the R&D sector. The production functions take on the following form:
\({\Theta }_{it}\) includes not only the output of secondary products but also the pollution emitted during the production of the secondary products; \({L}_{it}\) denotes the amount of labor input, where \(\sum_{i=1}^{n}{L}_{it}={L}_{t}\); \({\zeta }_{it}\left(j\right)\) denotes the jth tertiary product input and the energy input; and \({\rm T}_{it}\left(j\right)\) denotes the technological level of the jth tertiary product.
Given the price of tertiary products \({p}_{it}\left(j\right)\) and the price of labor (the wage) \({w}_{it}\), producers in the secondary product sector choose the optimal input of tertiary products \({\zeta }_{it}\left(j\right)\) and labor \({L}_{it}\) to maximize profit.
Tertiary products are produced by the R&D sector. Suppose that the probability of R&D success for the jth tertiary product is \({\mu }_{it}\left(j\right)\), and the failure probability is \(1-{\mu }_{it}\left(j\right)\). When R&D succeeds, the technology level increases to \({{\iota }_{i}{\rm T}}_{it}\left(j\right)\), where \({\iota }_{i}>1\). When R&D fails, the technology level remains \({\rm T}_{it}\left(j\right)\). The expression for the probability of R&D success is:
\({\chi }_{it}\left(j\right)\) denotes the R&D input, \({{\rm T}^{*}}_{it}\left(j\right)\) denotes the expected innovation level, and \({\nu }_{i}\) denotes the efficiency of R&D. For simplicity, R&D inputs are measured by the price of the secondary products, and we assume that one unit of secondary product is consumed to produce one unit of tertiary product.
Given the price of secondary products \({p}_{it}\), producers in the tertiary product sector choose the optimal quantity \({\zeta }_{it}\left(j\right)\), price \({p}_{it}\left(j\right)\), and R&D investment \({\chi }_{it}\left(j\right)\) for the tertiary products to maximize profit.
Technological effect of the digital economy
Digital inputs can improve the efficiency of factor use and technological R&D. And the efficiency of technological R&D can be expressed as a function of digital economy and the level to which digital technologies are applied in the industry.
where \(\kappa {, \beta }_{1}\), and \({\beta }_{2}\) are exogenous efficiency parameters, \({\mathrm{Digital}}_{it}\) is digital economy level, and \({\mathrm{Applied}}_{it}\) is the digital technology application level in the industry.
Let \({\int }_{0}^{1}{\rm T}_{it}\left(j\right)dj={\rm T}_{it}\), where \({\rm T}_{it}\) is the average level of technological progress, which can be expressed as:
The technological progress growth rate \({\Delta }_{it}\) in industry i can be expressed as:
Therefore, digital economy can improve the efficiency of industrial R&D \({\nu }_{i}\) and the probability of innovation success \({\upmu }_{\mathrm{it}}\) and can promote technological progress within an industry. Moreover, technology advances more rapidly when the industry is more digitalized.
Structural effect of the digital economy
The structural transformation refers to the change in the share of output and labor within each industry arising from intersectoral factor movements and reallocations. The output ratio between any two industries is as follows:
Supposing that labor can move freely between sectors, when the labor market is in equilibrium, wages in each sector are equal. Therefore, from Eq. (9), we can derive the following:
Furthermore, we can derive the following:
Therefore, changes in the labor or output shares of the product sectors are affected by the rate of sectoral technology progress \(\Delta\). Digital economy results in a different rate of sectoral technological progress, resulting in a structural transformation.
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Gao, W., Peng, Y. Energy saving and emission reduction effects of urban digital economy: technology dividends or structural dividends? . Environ Sci Pollut Res 30, 36851–36871 (2023). https://doi.org/10.1007/s11356-022-24780-1
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DOI: https://doi.org/10.1007/s11356-022-24780-1