The digital economy, significantly influenced by the rapid advancement of information and communication technologies (ICT), has demonstrated its financial and ecological benefits. This progression, driven by advanced technologies, has reduced greenhouse gas emissions, alleviated carbon burdens, and improved the overall efficacy of life-cycle cost (LCC) management. The power of innovation has established a strong foundation for environmental preservation, optimizing the economic dimensions of green development with tools like cloud computing and machine learning. This technological synergy has seamlessly integrated green technologies with conventional communication infrastructures, enabling continuous government monitoring and evaluation of environmental quality (Shobande et al., 2024). Additionally, the modern digital economy was distinguished by its more effective utilization of resources. Integrating data, knowledge, and other technological components significantly enhanced the efficacy and distribution of traditional resources. The development of the digital economy has provided many benefits, such as the reduction of transactional and informational costs, the promotion of regional industrial advancements, and a substantial decrease in greenhouse gas emissions (Peng et al., 2024). Furthermore, the digital economy’s intrinsic benefits and distinctive characteristics, including data sharing and the influence of global exchanges, facilitated the reduction of carbon emissions by reducing the number of redundant processes. Based on these observations, the research hypothesized that the digital economy had the potential to substantially improve the effectiveness of LCC by reducing greenhouse gas emissions and their associated activities.

This section explores the interconnectedness between digital innovation and sustainable city development, focusing on the role of green technology. Through an analysis of the digital economy’s influence, the section highlights how digital platforms and tools contribute to carbon footprint reduction, industrial transformation, regulatory advancements, and the promotion of sustainable living practices. Each subsection delves into specific areas where digital innovation indirectly fosters green technology and supports the transition toward more sustainable cities.

The fixed-impact approach works well for internal problems caused by missing factors. A subsequent fixed-effect foundation regression equation is built to confirm hypothesis 1 in the direct transfer scenario.

In this case, “i” denotes the city, and “t” denotes the year. Y stands for the parameter that is reliant on others α 0 signifies a fixed phrase, α 1 is the approximated variable of the electronic economy.

Dig it Shows a measure of the rise of the digital finance of city i duration of time t, and is the major contributing factor. X it Are the factors that influence the efficiency of environmentally friendlycity areas μ i stands for the town’s permanent impact δ t stands for the constant impact of a period of time, ε it stands for the phrase that denotes noise.

For the second study hypothesis, a mediation impact framework was built to talk about the potential indirect effects of the digital economy on LCC achievement, as well as the immediate implications. The following are the exact procedures: Assuming α1 is significantly different, we can build the electronic economy regression (Equation 2) for intermediate variables in combination and the LCC effectiveness predictor (Equation 3) for digital economic and intermediate variables separately. Here are the specifics, using the example.

Where M it stands as the moderating variable encompassing carbon sinks, low-carbon lifestyles, technological advances, manufacturing systems, environmental laws and regulations, and power structures. Assuming the importance of the factor b 1 in Equation 2 and c 2 is unaltered in Equation 3, yet the significance and ultimate worth of c 1 n Equation 3 drops dramatically compared to α 1 determined by the parameter in Equation 1 M it acts as a go-between for the digital economy and the efficiency of LCCs.

The present investigation utilizes multivariate outcome indices of LCC efficiency, which include total emissions of carbon (CE), carbon emissions intensity (CI), as well as low climate city efficiency (LCCE). Some research are among the prior investigations that have used carbon footprints, per capita carbon dioxide emission levels, greenhouse gas magnitude, and LCC productivity as signs to evaluate LCC efficiency. The effect of the Internet economy index on each of these metrics was computed in this research. It was noted, however, that greenhouse gas emissions and individual carbon emissions produce comparable outcomes. The three indices described earlier were thus chosen as the final metrics for evaluating LCC efficiency.

By taking the town’s carbon releases and multiplying them by a logarithm operation, the total carbon emissions can be determined using the approach put forward by particular, the methane emission caused by using natural gas NGas . Natural gas in a liquid state LPG and electricity Elec for use as energy sources. Building the Equation 4 in the following way:

Where σ 1 shows NGas the value of the carbon footprint coefficient is 2.1622 kg/m3. σ 2 Shows LPG A coefficient for carbon emissions, with an estimate of 3.1013 kg/kg. σ 3 means the coal-fired fuel’s greenhouse gas emissions factor, measured in 1.3023 kg/kWh; k provides a numerical representation of the percentage of the total output of electricity that comes from coal-fired generators.

Finding the proportion of greenhouse gas emissions to GDP is the first step in calculating the degree of carbon dioxide emissions.

The super-SBM approach, grounded in Xue et al.'s (2024) research, was used to determine the effectiveness of environmentally friendly cities.

The specific model expression is shown in Equation 5:

In Equation 5, the decision-making unit DMU for total factor carbon productivity is n, The input variable is x i , the expected output is y r , and the unexpected output is b t ; s i − , s r + , s t b − are the slack variables of input, expected output, and unexpected output, respectively; TFCP represents the green total factor productivity.

Figure 2 illustrates the conceptual framework used to analyze the impact of the digital economy on Low-Carbon City (LCC) efficiency. This framework outlines the critical input and output variables considered in the study.

The input variables included the labor force, encompassing both the size and quality of labor crucial for economic growth and development. Capital stock, comprising physical and human capital such as infrastructure, machinery, and technology, was also an essential input, facilitating production and innovation. Energy consumption, another critical input, represents energy resources necessary for economic activities, which could have significant environmental implications. The output variables were categorized into overall outputs and dimensional outputs. Overall outputs measured economic growth, indicated by the increase in GDP over time, reflecting the broader economic efficiency. Carbon reduction, signifying the decrease in greenhouse gas emissions, was another overall output, highlighting the environmental impact of financial activities. Dimensional output focused on specific areas, such as optimizing the industrial structure, which involved shifting toward more sustainable and efficient industrial sectors. Adjusting the energy structure referred to the transition from fossil fuels to renewable energy sources, while improving energy efficiency measured the reduction in energy consumption per unit of economic output. Additionally, increasing the carbon sink level indicated the enhancement of natural carbon sinks, such as forests, to absorb carbon dioxide. The research methodology employed a fixed-effect model to control unobserved heterogeneity across cities and periods, ensuring a more accurate estimation of the relationship between the digital economy and LCC efficiency. Mediation analysis was also utilized to examine the indirect effects of the digital economy on LCC efficiency through intermediate variables, including green technology innovation, industrial structure, environmental regulation, sustainable living, energy structure, and carbon sink capacity. The dependent variable, LCC efficiency, was measured using three indicators. Total carbon emissions (CE) represent a city’s overall amount of carbon dioxide. Carbon emissions intensity (CI) measures the amount of carbon emissions per unit of GDP. Low-Carbon City Efficiency (LCCE) assesses a city’s efficiency in reducing carbon emissions and promoting sustainable development.

The efficiency of low carbon consumption (LCC) efficiency can be substantially influenced by various control factors, as well as the digital economy. Incorporating these control factors into static models reduces estimation bias and ensures a precise evaluation. Parameters that frequently and significantly influence carbon dioxide emissions are identified in Table A1 of the accompanying document. Six critical criteria have been selected—Economic Development (ED), Population Size (PS), Science and Technology (ST), Financial Development (FD), Human Capital (HC), and Foreign Direct Investment (FDI)—in addition to economic growth, human resources, and foreign direct investment—because numerous studies have implemented a variety of metrics as control variables. Enhanced environmental awareness and an improved quality of life are frequently linked to economic development (ED), which is associated with economic growth. This study quantifies economic progress by calculating per capita income, which is derived from the logarithm of GDP per person. This methodology provides insight into the correlation between environmental practices and economic development. City areas’ population size (PS) can have a dual impact. On the one hand, increasing population may result in higher energy consumption and carbon emissions. Conversely, the aggregation effect of a larger population can enhance the efficacy of carbon dioxide emissions management. Population density, calculated as the logarithm of the total number of city residents divided by the total surface area of the city’s administrative zone, is used in this study to evaluate this aspect.

Science and technology (ST) are essential for improving city energy efficiency. Scientific and technological research can foster innovation and enhance energy management practices. Technological advancements are evaluated by comparing the proportion of GDP allocated to scientific and technological activities. Financial development (FD) can influence carbon emissions in both positive and negative ways. On the positive side, economic growth can promote environmentally friendly methods and improve manufacturing processes, thus reducing costs. However, it may also increase greenhouse gas emissions by boosting GDP growth and energy consumption, particularly of fossil fuels. In this investigation, the ratio of GDP allocated to financial institutions is used to assess economic development. Human Capital (HC) reflects a city’s capacity to cultivate talent and promote societal advancement. Increased educational attainment and awareness of environmental issues associated with human capital can stimulate innovation and reduce emissions. In this study, the proportion of students enrolled in postsecondary education relative to the total student population serves as a metric for human capital. Foreign Direct Investment (FDI) has a dual effect on environmental sustainability. While FDI can encourage the adoption of environmentally favorable technologies and reduce pollution, it may also exacerbate environmental issues by facilitating the relocation of polluting industries. The impact of foreign direct investment (FDI) on environmental stewardship and sustainability is evaluated by calculating the ratio of FDI to GDP.

The mediating factors employed in the preceding research are elaborated upon in Table A2 of the appendix. The data presented has identified five critical parameters as mediating variables: ecological regulations, economic infrastructure, environmentally friendly lifestyle, technological advancement, and electricity structure. Carbon Sink is a critical factor in implementing low-carbon consumption (LCC). Green technological innovations (GTI) significantly influence city economic growth. GTI has an immediate impact on the standard of city financial development, while creativity promotes high-quality growth. This study employs the number of green patent applications per capita (M1), as determined by the State-run Public Properties Administration of China, to represent GTI. M1 is calculated by dividing the total number of regional environmental patent applications by population derived from the World Intellectual Property Organization’s (WIPO) environmental technology list.

Another critical mediating factor is the infrastructure for industry (IS). A more optimized manufacturing system can facilitate the reduction of greenhouse gas emissions. The manufacturing structure parameter (M2) is determined by the percentage of local GDP produced by the S1, S2, and S3 sectors. Economic infrastructure’s efficacy is contingent upon this parameter. Ecological regulations (ER) substantially affect carbon emissions. Environmentally favorable legislation has the potential to reduce pollution and financial output, thereby reducing carbon emissions over time. This investigation evaluates environmental management by analyzing the abundance of ecological terminology in municipal government reports at the prefecture level (M3).

The Low-Carbon Lifestyle (LCL) reflects the growing public concern regarding carbon emissions, which is a result of the increasing rate of social and economic mobility. The proportion of electric vehicles per 10,000 people (M4) measures the environmental friendliness of city lifestyles. Carbon emissions are also influenced by energy structure (ES). The energy use pattern is critical because different energy sources have varying carbon contents. This facet is assessed by comparing the proportion of coal consumption to total energy consumption (M5). A carbon sink (CS) is indispensable for reducing carbon emissions and improving cities’ carbon storage capacity. Forests, vegetation, and pastures are examples of city green spaces contributing to carbon sequestration. The efficacy of a carbon sink is determined by the rate of vegetative cover in a specific area (M6). The coverage rate of a carbon sink is directly proportional to its effectiveness.

The autonomous factor is the Digital Economy Development (DED) extensive score. Ma et al. (2024) published a groundbreaking research paper on the digital economy in the esteemed magazine “The Field of Technology in Society” in 2022. This paper is cited in the DED comprehensive index and rating system. Other researchers have cited these findings, which serve as additional evidence of their significance. This research is consistent with a substantial corpus of prior research on the effectiveness of DEDs. For example, Zhang and Li (2024) employed the same set of metrics to assess the efficiency of DEDs. Consequently, Table 1 displays the selected indices to evaluate the efficacy of DED.

The measures of the digital economy developed for this research are captured through four key dimensions, each with a positive attribute indicating their beneficial impact on the digital economy. The first dimension, Internet-Related Employees, is measured by the rate of computer services and software workers as a percentage of the total workforce. A higher percentage suggests a robust digital economy, reflecting a significant portion of the workforce engaged in internet-related activities. The second dimension, Internet-Related Output, is assessed by the total number of telecom services per capita, measured in Yuan. This indicator signifies the economic contribution of internet-related services, with higher values indicating a well-developed digital infrastructure. The third dimension, Mobile Internet Users, is gauged by the density of mobile phone consumers relative to the total population. This measure reflects the penetration of mobile internet usage, where a higher density indicates widespread access and integration of mobile internet services. Lastly, Internet Penetration is measured by the number of internet users per one hundred people, highlighting the extent of internet usage within the population. A higher number signifies greater internet penetration, indicating the accessibility and integration of digital technology in everyday life. Collectively, these dimensions provide a comprehensive understanding of the digital economy’s development, emphasizing the importance of workforce engagement, economic Output, and user penetration in fostering digital economic growth.

The methods for data collection and the sources of the numerous indicators utilized in the research are delineated in Table 2. The indicators and their respective measuring methodologies are intended to capture various environmental and economic metrics aspects. The logarithm of carbon emissions is employed to quantify carbon emissions (CE) with data from the Statistics Yearbook of China Power Metropolis. The China city Statistical Yearbook calculates Carbon Intensity (CI) as carbon emissions per GDP. A super-SBM model is employed to determine Low-Carbon City Efficiency (LCCE) by analyzing primary data from various sources, such as the China Energy City Statistical Yearbook, China Statistical Yearbook, and China city Statistical Yearbook. The logarithm of per capita GDP is measured by the indicator C1, while C2 assesses the logarithm of the population relative to the city administrative district area. Both indicators are derived from the China city Statistical Yearbook. C3 assesses expenditures on science and technology as a percentage of GDP, C4 measures loans made to financial institutions relative to GDP, C5 calculates the ratio of college and university students to the total population, and C6 evaluates actual Foreign Direct Investment (FDI) as a percentage of GDP, all using data from the same yearbook. The State Intellectual Property Office of China and the China city Statistical Yearbook are the sources of M1, a metric that quantifies the number of green patent applications per capita. The three industrial structures determine M2, the PKULAW database determines M3, M4 is a percentage of the population that measures the density of electric cars, M5 is a calculation of coal consumption about overall energy consumption, and M6 is a measurement of the rate of green vegetation in finished areas, with data sourced from a variety of statistical yearbooks. Finally, the China city Statistical Yearbook and the China Energy City Statistical Yearbook are used to calculate Digital Economy Development (DED) using a PCA model. Collectively, these indicators establish a comprehensive framework for examining the relationship between economic activities and environmental impacts in city areas of China.

The sample size is a critical factor in applying these models, as small numbers may result in unpredictability. It is imperative to select an appropriate sample size to ensure the precision and dependability of the investigation. Research has demonstrated that the Internet of Things impacts carbon intensities. The specific result analysis of the data presentation is as follows:

Firstly, the development process and mode transformation. In the past 15 years, 271 cities in China have undergone significant evolution from initial exploration to systematic deepening in using big data to promote low-carbon development. In the early stages, big data applications were mainly focused on policy pilots and infrastructure construction in individual fields, such as industrial energy consumption monitoring. With the passage of time, its application scope has rapidly expanded and its depth has continuously increased, gradually evolving into a core force driving the overall carbon reduction of cities. This process has achieved a systematic transformation from decentralized and single point technological attempts to comprehensive and deep integration with urban energy structure, industrial structure, transportation system, and governance mode, marking a fundamental shift from experience driven to data-driven and intelligent low-carbon governance mode.

Secondly, the key driving forces and urban differences. The key driving force behind this transformation process lies in the synergy between national top-level design and local pilot innovation. The combined effect of policies such as “smart cities,” “national big data comprehensive pilot zones,” and “low-carbon pilot cities” provides a clear policy framework and practical field for big data to empower low-carbon development. However, the effectiveness of big data emission reduction shows significant differences among 271 cities. Usually, developed eastern regions and mega cities, with their superior economic foundation, technological talents, and infrastructure, can more efficiently utilize big data, forming a pattern of “high in the east and low in the west” and “big cities leading.” The technological paths of resource-based cities and tertiary industry dominated cities also vary depending on their endowments.

Thirdly, future trends and prospects. Looking toward the future, the path of “digital low-carbon” in Chinese cities will present a trend of deep integration and refined management. Technologically, artificial intelligence and fifth generation mobile communication will be more closely integrated with carbon management to enhance the accuracy of prediction and optimization. In terms of management, the granularity of carbon emission control will continue to be refined, from city level platforms to park, community, and even building levels, achieving more extreme energy conservation and emission reduction. At the same time, market-oriented mechanisms such as green finance and carbon trading markets will be deeply linked with big data platforms to inject sustained economic momentum into the low-carbon transformation of cities, ultimately promoting the construction of smart, green, and resilient future cities.

Spatial geographical analysis of narrow with results of the fundamental analysis, which addresses endogenous variables issues, are presented in Table 3. The table illustrates the impact of the digital market on the efficacy of LCCs after accounting for characteristics such as management, city-fixed consequences, and year-fixed consequences. Thus, the data presented in columns (1) and (2) of Table 3 indicate that the anticipated impacts on total and city carbon dioxide emissions from the digital economy are statistically significant and negative at the 1 and 5% levels. The utilization of technology substantially reduces greenhouse gases at the city level, both in magnitude and in totality, on a national scale. At the 5% significance level, the computed coefficients in columns (3) of 0.030 suggest a beneficial correlation between the efficacy of LCCs and the digital sector. To be more precise, the efficacy of a town’s LCC increases by 0.030 units for every 5% increase in the Internet economy index. The findings further support the initial hypothesis. Financial growth and the density of populations, which are considered controllable factors, have a substantial impact on the release of carbon and concentration. As economic and demographic factors increase, certain regions experience a decrease in emission intensity and an increase in greenhouse gas emissions. The efficiency of LCCs is influenced by the amount of money spent on research and development—precisely, a higher level of R&D expenditure results in higher LCC efficiency.

The study employs a series of robustness tests to further validate the accuracy of the empirical evidence. The analysis begins with a reduction strategy, where a new estimation experiment was conducted, omitting measurements related to carbon emission effectiveness at the extreme 1% level due to the impact of the highest values on the overall sample. The results in columns (1)–(3) of Table 4 indicate that LCC effectiveness is enhanced, and the digital marketplace contributes to reducing the total amount and degree of greenhouse gas emissions in metropolitan areas. These outcomes are consistent with the findings of the initial regression analysis.

Table 4 illustrates the results of the resilience analysis, emphasizing the correlation between the development of the digital economy (DED) and various economic and environmental indicators. The study employs six models, each of which analyzes distinct combinations of dependent variables: carbon intensity (CI), low-carbon city efficiency (LCCE), and the logarithm of carbon emissions (lnCE). The results suggest that DED has a substantial negative impact on carbon intensity (CI) and carbon emissions (lnCE), with coefficients of −0.081 and −0.038 in models (1) and (2), respectively, and −0.068 and −0.042 in models (4) and (5). This implies that the digital economy’s advancements contribute to reducing carbon emissions and intensity. In contrast, the coefficients of 0.031 and 0.033 in models (3) and (6) suggest that digital economy initiatives improve low-carbon efficiency in cities, indicating that DED positively impacts LCCE. Mixed effects are observed for other variables, including the logarithm of economic development (lnED) and population size (lnPS). lnED has a positive and substantial influence on carbon emissions but a negative effect on carbon intensity. This implies that economic growth increases emissions but reduces emissions per unit of GDP. DPS also has a similar effect, increasing emissions while decreasing carbon intensity, underscoring the intricate relationship between population growth and environmental impact. The current investments may need to be more effectively translated into low-carbon efficiencies, as science and technology expenditures (ST) negatively influence LCCE. Foreign direct investment (FDI), human capital (HC), and financial development (FD) exhibit largely insignificant effects in all models.

The analysis incorporates fixed effects for city and year to ensure that the results represent unobserved heterogeneity across cities and over time. The adjusted R-squared values suggest that the included variables effectively reflect the variations in these outcomes, particularly for lnCE and CI, indicating a high level of explanatory power for the models. In general, the results emphasize the digital economy’s positive impact on environmental sustainability and the necessity of additional enhancements to improve the efficacy of low-carbon cities.

The regression analysis from the benchmarking research reveals that the digital economy has the potential to enhance low carbon consumption (LCC) efficiency while concurrently diminishing both the total amount and the severity of carbon emissions. This dual benefit raises the question of how such improvements are achieved. To address this, Models (2) and (3) were employed to assess the underlying processes and mechanisms through which the digital economy exerts its influence. The detailed findings of these evaluations are presented in Table 5, which elucidates how integrating digital technologies and platforms facilitates more efficient carbon management and promotes environmental sustainability. At the 5 % barrier, the estimated coefficient for the digital economy in column (1) of Table 5 is 0.484, indicating a significant favorable influence. This demonstrates that the internet marketplace has the potential to foster ecologically sustainable technological advancements. They are adding innovation in green technology to the data in column (2), which results in an unfavorable core explaining the coefficients of the variable, which subsequently decreases. These findings indicate that implementing more environmentally friendly technology and innovative thinking in the digital industry could mitigate the emission of carbon dioxide. Furthermore, the quantity of the significant descriptive variable decreases dramatically while maintaining a high coefficient when green progress in technology is represented in column (4). This illustrates how environmentally aware technical advancements might help the digital economy become more efficient regarding life cycle cost. Analysis of column (1) reveals an unforeseen absence of a statistically significant correlation between the Internet economy and the industrial system, notwithstanding the positive impact. The strategies used by the federal government to promote the growing digital economy have stimulated increased expenditure in several industries, promoting transformations in the manufacturing system and decreasing greenhouse gas emissions. Another plausible rationale is the rapid advancement of digital technologies such as cloud storage and artificial intelligence (AI).

Nevertheless, they represent the optimal result. The conclusions of this study indicate that the electronic economy has a modest yet beneficial impact on the economic structure. Geng et al. (2024) present pertinent statistical evidence suggesting that altering the composition of the manufacturing industry will not lead to a total mitigation of carbon dioxide emissions. Yin et al. (2024) argue that significant changes can influence the effectiveness of carbon emission reduction efforts resulting from industry restructuring in primary and tertiary sectors and local economic and corporate structures. The findings in Table 5 support the claim that carbon-free travel and more stringent environmental regulations are outcomes of the digital marketplace. However, these factors alone have not achieved the desired level of carbon reduction. Energy consumption also exerts a mediating effect. Modifying the fundamental principles of energy consumption demonstrates that digital commerce does not substantially reduce greenhouse gas emissions. Firstly, the country’s energy consumption system depends significantly on high-carbon energy sources due to limitations imposed by natural resources. This poses challenges in promptly implementing changes and results in little immediate visibility of the benefits of reducing greenhouse gas emissions. Furthermore, attempting to improve the electrical power system by increasing renewable energy is expensive and imposes additional economic strain, complicating efforts to mitigate climate change. Lastly, the function of carbon sinks as intermediates is studied. While the emergence of the internet promotes carbon sinks, the consequences of this phenomenon may require a considerable amount of time to become evident. The process’s study suggests that advancements in green technology within the digital sector have the potential to reduce greenhouse gas emissions and enhance LCC production effectively. Some body of evidence exists that substantiates hypothesis 2.

The relationship between these factors is expected to vary due to the significant regional variation in carbon emissions and technological innovation levels. By employing a variance analysis that takes into account three dimensions: local geographic region, municipal size, and resource type, this study improves comprehension of how the digital market affects the effectiveness of LCCs. Table 6 examines the heterogeneous impacts of the digital economy on low-carbon city efficiency (LCCE) across different regions in China, categorized by the National Bureau of Information into eastern, central, western, and northeastern areas. The table reveals that the effects of the digital economy on carbon emissions and LCCE vary significantly by region. In the Eastern Region, digital economy development (DED) has a modest negative impact on the logarithm of carbon emissions (lnCE) with a coefficient of −0.076, significant at the 10% level, indicating a slight reduction in emissions. However, its effects on carbon intensity (CI) and LCCE are not statistically significant, suggesting limited influence on these aspects in this region. The adjusted R-squared values indicate a robust model fit for lnCE (0.929) but moderate for CI (0.546) and LCCE (0.579). In the Central Region, DED shows a more pronounced negative impact on both lnCE (−0.152) and CI (−0.085), both significant at the 5% level, demonstrating a substantial reduction in emissions and intensity. Additionally, DED positively influences LCCE (0.062), which is also significant at the 5% level, indicating improved low-carbon efficiency. The adjusted R-squared values are high for lnCE (0.893) and moderate for CI (0.632) and LCCE (0.555), suggesting an excellent explanatory power of the models. The Western Region shows no significant effects of DED on lnCE, CI, or LCCE, with coefficients of −0.024, −0.075, and −0.002, respectively, indicating that the digital economy’s influence is minimal in this area. The adjusted R-squared values are high for CI (0.695) but lower for lnCE (0.878) and LCCE (0.455), reflecting varying model fits. In the Northeast Region, DED does not significantly affect lnCE (−0.027) or CI (0.177), but it has a positive, albeit not significant, impact on LCCE (0.077). The adjusted R-squared values are high for lnCE (0.903) but moderate for CI (0.559) and LCCE (0.497), indicating a reasonable fit for the models.

Table 7 explores the heterogeneous impacts of the digital economy on low-carbon city efficiency (LCCE) and carbon emissions across different city sizes, categorized into large cities and medium-sized and small cities. The table presents the effects of digital economy development (DED) on the logarithm of carbon emissions (lnCE), carbon intensity (CI), and LCCE. In large cities, the impact of DED on lnCE is negative (−0.038), but not statistically significant, indicating that the digital economy does not have a substantial effect on reducing carbon emissions in these areas. Similarly, the impact on CI is also negative (−0.004) and not significant, suggesting a limited influence on carbon intensity. However, DED shows a positive but non-significant effect on LCCE (0.021), indicating a potential, though not statistically confirmed, improvement in low-carbon efficiency. The adjusted R-squared values are high for lnCE (0.920), suggesting a robust model fit but moderate for CI (0.637) and LCCE (0.566). In contrast, medium-sized and small cities show a more pronounced impact. DED has a significant negative effect on lnCE (−0.073, p < 0.10), indicating that the digital economy significantly reduces carbon emissions in these cities. The effect on CI is negative (−0.052) but not statistically significant, suggesting some reduction in carbon intensity, though not confirmed by statistical significance. The impact on LCCE is positive and significant (0.030, p < 0.10), indicating that the digital economy effectively enhances low-carbon efficiency in medium-sized and small cities. The adjusted R-squared values are slightly lower for lnCE (0.858) compared to large cities but higher for CI (0.681) and moderate for LCCE (0.528), suggesting a reasonable fit for the models.

The impact of digital finance on LCC efficiency varies among city kinds of resources, as shown in Table 8. It explores the heterogeneous effects of digital economy development (DED) on carbon emissions and low-carbon city efficiency (LCCE) in resource-dependent and non-resource-dependent cities. The analysis reveals distinct differences in how these cities respond to the digital economy. In resource-dependent cities, DED has a significant negative impact on the logarithm of carbon emissions (lnCE) with a coefficient of −0.111, which is important at the 10% level and indicates a reduction in emissions. It also significantly reduces carbon intensity (CI) with a coefficient of −0.090, which is significant at the 5% level, suggesting that the digital economy effectively decreases the amount of carbon emissions per unit of GDP. Furthermore, DED positively affects LCCE with a coefficient of 0.054, which is significant at the 5% level and indicates improvements in low-carbon efficiency. The adjusted R-squared values are high for lnCE (0.867) and CI (0.766) and moderate for LCCE (0.487), suggesting the models explain a substantial portion of the variance in these outcomes. Conversely, in non-resource-dependent cities, the effects of DED on lnCE (−0.053) and CI (−0.023) are negative but not statistically significant, indicating that the digital economy’s impact on reducing emissions and intensity is less pronounced in these areas. The effect on LCCE is positive (0.018) but also insignificant, suggesting limited improvements in low-carbon efficiency. The adjusted R-squared values are high for lnCE (0.928) and moderate for CI (0.612) and LCCE (0.569), indicating a good fit for the models.

The findings support both Hypothesis 1, 2. For Hypothesis 1, using a fixed-effect model effectively captures the impact of direct transfer mechanisms by controlling for unobserved heterogeneity across cities and periods, leading to statistically significant findings. The analysis demonstrates that the digital economy, represented by the Digital Economy Development (DED) index, significantly reduces carbon emissions and carbon intensity while improving low-carbon city efficiency (LCCE), as shown by the negative coefficients for carbon intensity and emissions and positive coefficients for LCCE. This confirms the fixed-effect model’s ability to isolate the effects of the digital economy on these outcomes. For Hypothesis 2, the content reveals that the internet-based economy enhances LCC effectiveness and reduces greenhouse gas emissions by promoting innovations in green technologies. The study’s mechanism analysis shows that the digital economy facilitates more efficient carbon management and promotes environmental sustainability through green technology innovation, evidenced by the significant positive impact of DED on LCCE and the reduction in carbon emissions and intensity. These findings confirm that the digital economy’s advancements enhance low-carbon city effectiveness and reduce greenhouse gas emissions, aligning with the hypothesis.