Abstract:IntroductionPromoting the development of digital technology is an important step in meeting the challenge of global climate change and achieving carbon peaking and carbon neutrality goals.MethodsBased on panel data of Chinese cities from 2006 to 2020, this paper used econometrics to investigate the impact and mechanism of digital technology on carbon emissions.ResultsThe results showed that digital technology can significantly reduce carbon emission intensity and improve carbon emission efficiency. These resul… Show more
“…Compared with other estimation methods, this method can obtain consistent standard errors in the control of heteroscedasticity and autocorrelation. When the time dimension is gradually increased, the standard errors are robust to the general form of sectional correlation and time correlation (Driscoll and Kraay, 1998; Shen et al, 2023b). According to research H1, the two-way fixed effect (TWFE) was used to calculate Eqs.…”
Section: Resultsmentioning
confidence: 99%
“…To eliminate the potential endogenous problem, this study used the instrumental variable method to deal with it. Referring to the ideas of previous studies, this study used the number of post offices per 10000 people in each region in 1984 as the instrumental variable (Zhao et al, 2020; Shen et al, 2023b). The instrumental variable selected in this study was the cross-section data for 1984, but that data does not change with time.…”
Improving energy efficiency is crucial for achieving the carbon peaking and carbon neutrality goals. The digital economy, which is characterized by big data, artificial intelligence, the internet of things, and a new generation of mobile Internet, has quietly penetrated all aspects of the economy and society, profoundly changing the means of production and lives of human beings. Digital technologies have great potential to improve the global energy system's security, productivity, efficiency, and sustainability. Based on the panel data of 30 provinces in mainland China from 2006 to 2021, this study divided energy efficiency into total and single factor energy efficiency. The two-way fixed-effect model and the Driscol-Kraay method were used to adjust the standard error test in order to examine the impact of digital technology represented by industrial robots on energy efficiency and its path mechanism. Studies have shown that digital technology can significantly improve total factor energy efficiency and reduce energy intensity per unit of GDP. This conclusion was found to be still valid after the robustness test using feasible generalized least squares, time-varying difference in difference and fixed effect space Durbin model. The results of the mechanism test show that digital technology can improve energy efficiency by increasing the degree of industrial virtual agglomeration and the channels of foreign direct investment. This paper provides a valuable discussion on how information technology advances can improve energy efficiency in the era of the digital economy. The conclusions will help relevant market players to formulate policies and measures and corporate strategies to improve energy efficiency. At the same time, it also deepens the theoretical understanding and mechanism path of digital technology's impact on energy consumption.
“…Compared with other estimation methods, this method can obtain consistent standard errors in the control of heteroscedasticity and autocorrelation. When the time dimension is gradually increased, the standard errors are robust to the general form of sectional correlation and time correlation (Driscoll and Kraay, 1998; Shen et al, 2023b). According to research H1, the two-way fixed effect (TWFE) was used to calculate Eqs.…”
Section: Resultsmentioning
confidence: 99%
“…To eliminate the potential endogenous problem, this study used the instrumental variable method to deal with it. Referring to the ideas of previous studies, this study used the number of post offices per 10000 people in each region in 1984 as the instrumental variable (Zhao et al, 2020; Shen et al, 2023b). The instrumental variable selected in this study was the cross-section data for 1984, but that data does not change with time.…”
Improving energy efficiency is crucial for achieving the carbon peaking and carbon neutrality goals. The digital economy, which is characterized by big data, artificial intelligence, the internet of things, and a new generation of mobile Internet, has quietly penetrated all aspects of the economy and society, profoundly changing the means of production and lives of human beings. Digital technologies have great potential to improve the global energy system's security, productivity, efficiency, and sustainability. Based on the panel data of 30 provinces in mainland China from 2006 to 2021, this study divided energy efficiency into total and single factor energy efficiency. The two-way fixed-effect model and the Driscol-Kraay method were used to adjust the standard error test in order to examine the impact of digital technology represented by industrial robots on energy efficiency and its path mechanism. Studies have shown that digital technology can significantly improve total factor energy efficiency and reduce energy intensity per unit of GDP. This conclusion was found to be still valid after the robustness test using feasible generalized least squares, time-varying difference in difference and fixed effect space Durbin model. The results of the mechanism test show that digital technology can improve energy efficiency by increasing the degree of industrial virtual agglomeration and the channels of foreign direct investment. This paper provides a valuable discussion on how information technology advances can improve energy efficiency in the era of the digital economy. The conclusions will help relevant market players to formulate policies and measures and corporate strategies to improve energy efficiency. At the same time, it also deepens the theoretical understanding and mechanism path of digital technology's impact on energy consumption.
“…Does digital economic development have a direct impact on lowcarbon sustainable development? Digital economic development is a modern economic form based on digital technology, with information, knowledge, and intellectual capital as its elements, information networks as its carrier, and digitization as its feature (Shen et al, 2023). The rapid development of the digital economy brings new opportunities and challenges to economic growth, industrial upgrading, employment innovation, and international competition (Volkova et al, 2019).…”
Section: Putting Forward Carbon Peaking and Carbon Neutrality Goalsmentioning
Low‐carbon sustainable development is considered an essential strategy for achieving economic growth and environmental protection, requiring a fundamental transformation of the industrial structure, including developing clean technologies, promoting energy efficiency, and adopting sustainable production and consumption patterns. Simultaneously, the evolving digital economy is acknowledged as an essential driver in optimizing the industrial structure. Therefore, can digital economy development reshape the industrial structure and lead to low‐carbon sustainable development? The main purpose of this paper is to examine the mediating role of industrial structure upgrading in the relationship between the digital economy and low‐carbon development. Its ultimate purpose is to explore more possible paths to achieve low‐carbon development. This paper builds a regression model based on the data of cities. We found that digital economic development can directly promote low‐carbon sustainable development, a conclusion that still holds after endogeneity discussion and robustness testing. Additionally, digital economy development can promote industrial structure upgrading and thus promote low‐carbon sustainable development, meaning that industrial structure upgrading is an effective mechanism for digital economy development to promote low‐carbon sustainable development. This paper provides empirical evidence for the positive environmental externalities of digital economic development and establishes a basic research framework of “digital economic development → industrial structure upgrading → low‐carbon sustainable development.”
“…Noticeably, CO 2 is the most important contributor to climate change and global warming, evidenced by the fact that the temperature has risen by more than 1˚C to 1.2˚C since the preindustrial era began [3,4]. Considering the severe consequences of carbon emissions on human survival and the sustainability of the planet Earth [5], the United Nations also named climate change a 'defining crisis of our time' [6]. In the current global environmental crisis, carbon emissions efficiency has become a key global concern.…”
Section: Introductionmentioning
confidence: 99%
“…Previous findings indicate that digitalization [5], technological innovation [20], financial [20], economic [24] and industrial growth [7] are all major contributing factors to carbon emissions. To the best of our knowledge, this study is the first of its nature to explore the dynamic relationship among innovation and technology-based thematic investing, carbon emissions efficiency time and frequency domains.…”
This study is aimed at investigating the asymmetric and time-frequency co-movements and the hedge or safe-haven properties of carbon efficient indices, the MSCI ACWI Sustainable Impact, and MSCI World EGS indices, in relation to technology and innovation-themed investments. In doing so, the ADCC-GJR-GARCH and wavelet coherence techniques are applied to a daily return series ranging from January 2019 to January 2023. Findings of the ADCC-GJR-GARCH model show negative and insignificant asymmetric linkage among underlying indices during the sample period. The S&P 500 carbon efficient index (CEI) acts as a strong hedge or safe-haven for technology and innovation-themed indices during tranquil and tumultuous periods. The MSCI ACWI Sustainable Impact, MSCI World EGS, and carbon efficient indices except for S&P 500 CEI exhibit weak hedge or safe-haven attributes. Wavelet coherence reveals negative (positive) co-movements between the thematic and carbon efficient indices in short-term (medium-term and long-term) horizons with consistent leading behavior of thematic indices to carbon efficient indices outcomes. It justifies the presence of short-lived hedging or safe-haven characteristics in the thematic domain for investors. These strong and weak hedge or safe-haven characteristics of low carbon and sustainability indices reveal that adding low carbon efficient and sustainable investments to a portfolio result in considerable diversification benefits for investors who tend to take minimal risk in both tranquil and tumultuous periods. The current findings imply that financial institutions, thematic investing companies, and governments need to encourage carbon efficient technology transfer and innovation-themed investments by increasing the fund allocations in underlying asset classes. Policy-making and regulatory bodies can encourage investors to make carbon-efficient and thematic investments and companies to issue carbon-efficient stocks or investments to safeguard social and economic risks during fragile periods. These investments can offer greater opportunities to combat the intensity of economic shocks on portfolios for responsible or sustainable investors.
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