Green Technological Progress Research Articles

Revealing the effectiveness of green technological progress and financial innovation on green economic growth: the role of environmental regulation.

Green economic growth is the best alternative strategy for sustainable development. Existing literature investigated the determinants of green economic growth in China and provides mixed results. Thus, our study explores the impact of green environmental technology, financial innovation, and environmental regulations on green economic growth by controlling the impact of renewable energy consumption, trade, and education. The study explores the symmetric and asymmetric associations by employing ARDL and NARDL approaches. The ARDL long-run findings display that green environmental technologies, environmental regulations, and financial innovations positively and significantly contribute to green economic growth. However, the NARDL long-run findings infer that positive shock in green environmental technology, financial innovation, and environmental regulation exerts a significant and positive impact on green growth, while negative shock in green environmental technology, financial innovation, and environmental regulation has an insignificant impact on green growth. Based on the findings, the study delivers important policy implications to promote green economic growth in China.

Substantive green innovation or symbolic green innovation? The impact of ER on enterprise green innovation based on the dual moderating effects

Based on the existing research on environmental regulation (ER) and enterprise innovation, this paper separates green innovation ability from general technological innovation and divides it, from the perspective of motivation, into substantive green innovation (SUBGI) and symbolic green innovation (SYMGI). This paper employs panel data of China's A-share listed enterprise from 2008 to 2018 in a difference-in-difference-in-difference model to construct a quasinatural experiment on the impacts of ER and the green innovation strategy of enterprises. Government subsidies and regulatory capture are used to explore the mechanism between ER and innovation behavior. The results show that ER has a significantly positive effect on green innovation, but its impact on SYMGI and SUBGI decreases. Under the constraints of the ER policy, government subsidies incentivize the green innovation of enterprises, but they are not the main reason for the difference between the two innovation behaviors. Regulatory capture plays a negative moderating role in ER promoting enterprises’ SUBGI and has no significant impact on SYMGI, which is the key factor leading to the difference between the two innovation behaviors. The heterogeneity test suggests a pronounced promotion effect of ER on state-owned enterprises, large enterprises, and growing enterprises. This study provides momentous policy implications for making rational use of environmental policies in promoting enterprises’ green innovation capability, especially high-quality green innovation behavior aimed at promoting enterprises’ green technology progress and gaining competitive advantages.

Green finance, technological progress, and ecological performance-evidence from 30 Provinces in China.

The interaction between green finance and other factors, such as ecological environment, has been a research hotspot nowadays. Especially, the reasonable guiding of capital into energy conservation and environmental protection industries would greatly affect those factors, so as to the relation between them. This paper aimed to analyze the relationships between green finance, technological progress, and ecological performance quantitatively. The entropy method was used to respectively construct the system of index for green finance and technological progress, and index for ecological performance was measured by the super-SBM model. The panel vector autoregressive (PVAR) model was selected to empirically analyze dynamic relationships based on datasets from 30 provinces in China during 2008-2019 period. The results told that (1) from 2008 to 2019, China's overall level of green finance, technological progress and ecological performance increased to varying degrees. Spatially, the areas with high-developed green finance greatly coincided with those such as large cities or the eastern coast that had good financial development. The distribution of technological progress index were similar, except some underdeveloped areas with relatively advanced scientific research institutes. The ecological performance, however, was high in the South and low in the north. (2) In the lag for 3 years, the influence of green finance on ecological performance in different regions was all positive for that all the coefficient symbols that passed the significance test were above 0, while that on technological progress was negative first and then positive. And the effects of technological progress on ecological performance were positive in ecological regions and negative in low ecological regions (0.0893 and -0.1211 in the case of three-stage lag respectively). (3) The contribution of green finance to ecological performance was high according to the results of variance decomposition, maintained at about 30%, and that of technological progress increased year by year (from 0.000 to 0.039). Therefore, we proposed to strengthen the development of green finance in underdeveloped regions. The emphasis should be laid on the researches and applications of green technology, the formulation of financing policies in innovation compensation and the establishment of a dynamic monitoring system for the ecological environment.

Could Green Technology Innovation Help Economy Achieve Carbon Neutrality Development–Evidence From Chinese Cities

China’s 12th Five-Year Plan emphasizes green technological advances in energy conservation, which provides a feasible quasi-natural experimental node to study the role of green technological innovation in influencing the achievement of carbon neutrality. The difference-in-difference model examines whether China’s electricity consumption efficiency has improved since the 12th Five-Year Plan and reveals the role of green technology innovation in this process. Specifically, this paper takes 216 cities in China from 2003 to 2016 as the study sample, the midpoint between China’s 11th and 12th Five-Year Plans as the quasi-natural starting point, and uses the top 50 cities in terms of the number of listed companies as the quasi-natural experimental group. The results show that China’s electricity consumption efficiency has improved significantly since the 12th Five-Year Plan, supported by different robustness tests. The mechanism analysis finds that green technology innovation positively affects energy efficiency but is not the best option for cities with many listed companies. Cities with many listed companies can achieve energy savings by adjusting their industrial structure. Energy conservation and emission reduction policies should be formulated according to the city’s situation and give full play to green technology progress and industrial transformation and upgrading, which is of great significance to achieving carbon neutrality.

How does artificial intelligence affect green economic growth?—Evidence from China

As the driving force behind the new wave of technological advancement, artificial intelligence (AI) paves the way for a new era of green economic growth. This paper uses the case of China and constructs a relevant mathematical model to propose the two-way impact of AI on green total factor productivity (GTFP) and systematically analyze the impact of AI on green economic growth. The main findings of this study are that AI has a significant “U-shaped” effect on GTFP, which was estimated using a nonlinear dynamic panel regression model. The analysis of regional heterogeneity shows that improving AI in resource-rich areas can boost GTFP and break the “resource curse”. The “productivity paradox” arises, according to the industry heterogeneity analysis, because the current low level of intelligence in resource-intensive and labor-intensive industries fails to improve GTFP. This paper also breaks down GTFP into green technological progress and green efficiency improvement effects to figure out how AI affects green economic growth.

Study on green output bias of China's mariculture technological progress.

With the rapid development of mariculture, environmental pollution is becoming more and more serious. It is particularly important to study the green output bias of technological progress for the long-term sustainable development of mariculture. Based on the input-output data of the mariculture in 10 Chinese coastal areas from 2008 to 2018, the DEA-Malmquist index decomposition method is used to calculate the output-biased technological progress index and combines with the change of marginal substitution rate of output factors in different periods to identify the bias of technological progress between output factors and analyze the green output bias of mariculture technological progress. The results show that green technological progress is a crucial factor affecting green total factor productivity's growth in China's mariculture. During the study period, the number of regions where technological progress tends to reduce pollutant emissions shows a fluctuating upward trend, and the degree of technological progress toward green output is high. There are obvious differences among regions. The green output bias of technological progress in the South China Sea area is the highest, followed by the East China Sea area, and the Bohai Sea area is poor. Specifically, the green output bias of technological progress in Fujian, Guangdong, and Hainan is higher, while the green output bias of technological progress in Tianjin, Hebei, Shandong, and Guangxi is lower. According to the research results, corresponding policy suggestions are provided to realize the green and sustainable development of China's mariculture.

The Dynamic Impact of Digital Economy on Carbon Emission Reduction: Evidence City-level Empirical Data in China

The digital economy is of great significant for carbon emission reduction. To estimate the impact on carbon emissions of digital economy, this paper conducts nonlinear analysis with the combination of spatial DURBIN model (SDM) and panel threshold model (PTM). Moreover, this paper decomposes the carbon emissions reduction effect into direct part and spatial spillover part, further analyzes the action mechanism from the perspective of technology progress, energy use and industrial structure. The empirical results indicate that the digital economy and carbon emissions have inverted U-shaped relationship. Similarly, the spatial spillover effect of digital economy on carbon emissions is also inverted-U shape. The digital economy increases first and then reduces the carbon emissions. The robustness tests and endogenous analysis both support above conclusions. Compared with the indirect effect, the direct effect of digital economy on carbon emissions is significantly greater. Moreover, the long-term effect of digital economy on carbon emissions is greater than the short-term effect. The impact of digital economy on carbon emissions exists regional heterogeneity. Different from the eastern and central regions, the digital economy in western region promotes carbon emissions monotonically. The impact of digital economy on carbon emissions faces significant resource endowment thresholds, urban scale thresholds and innovative capability thresholds. The increase of energy use and non-green technology progress are the main paths to influence local carbon emissions in short term, while the green technology progress and industrial structure upgrade become dominant paths in long term. The active demonstration of industrial structure upgrade and technology spillovers drive the spillover effect of digital economy on carbon emissions in long term. This paper suggests that, the realization of carbon peak and carbon neutral needs to strengthen the digital economy and promote the regional cooperation in environmental governance. The green integration of digital economy and traditional industries is of great significant for carbon emission reduction.

Evaluation and Influencing Factors of China’s Agricultural Productivity from the Perspective of Environmental Constraints

Based on provincial panel data for the past 15 years in China, the SBM-ML index method was used to measure agricultural productivity under the environmental-constraint perspective with agricultural surface source pollution as the non-desired output. A dynamic panel regression model was used to empirically analyze the factors influencing agricultural productivity to provide a reference for formulating policies to alleviate the conflict between economic development and environmental pollution. The results show that the green total factor productivity of Chinese agriculture exhibits a slow, incremental trend year by year. The growth of green total factor productivity in agriculture mainly comes from the increase in the rate of green technological progress. In terms of geographical disparity, the eastern, central, and western regions show a high-to-low gradient of agricultural green total factor productivity. Agricultural green total factor productivity showed a significant positive spatial correlation in some years. As for the influencing factors, foreign trade in agricultural products is conducive to enhancing green total factor productivity in agriculture, whereas foreign direct investment in agriculture and agricultural technology input inhibit the growth of green total factor productivity in agriculture. This research also found a significant U-shaped relationship between environmental management inputs and green total factor productivity in agriculture. Accordingly, suggestions are provided to optimize the international trade structure of agricultural products, selectively introduce high-quality green foreign investment projects, drive the efficiency of R&D investment through digital technology, and increase investment in special funds for agricultural pollution control.

How do heterogeneous R&D investments affect China's green productivity: Revisiting the Porter hypothesis

Currently, China's environmental pollution control investment and R&D investment have increased at a remarkable speed, exceeding the GDP growth rate. Against this background, determining the impact of different orientations of R&D investment on green total-factor productivity (GTFP) is essential for overcoming the dual dilemma of resource depletion and environmental degradation. By applying the perpetual inventory method (PIM), meta-frontier DEA method and mediation effect test methods, this paper empirically tests the effects and mechanisms of environmentally induced R&D (ER&D) and traditional R&D (TR&D) on promoting GTFP during 2004–2019 in China at the provincial level. The results show that (1) ER&D investment has significantly promoted the growth of GTFP, while TR&D's promoting effects on GTFP are not significant; (2) ER&D promotes the growth of GTFP through the three channels of emission reduction, clean energy consumption, and green technology progress; and (3) from the perspective of regional heterogeneity, ER&D's promoting effects on GTFP in the eastern provinces are higher than the central and western provinces, and the promoting effects in the northern provinces are higher than in the southern provinces. From the perspective of pollution degree heterogeneity, ER&D's promotion of GTFP shows an inverted U-shaped characteristic as the pollution level increases. This means that, in the process of China's environmental governance, it is necessary to increase ER&D investment and guide green innovation to serve pollution control to achieve sustainable and high-quality economic development.

The effect of manufacturing intelligence on green innovation performance in China

Accelerating the intelligent transformation of manufacturing industry is an important strategic choice to realize the green innovation transformation. Based on the perspectives of static efficiency and dynamic productivity, this paper analyzes the effect of manufacturing intelligence on green innovation performance and its internal mechanism from theoretical and empirical levels by using the dynamic spatial lag model (DSAR), mediating effect model and moderating effect model. The results show that: In the whole country, manufacturing intelligence has a significant promotion effect on green innovation performance; The reason why manufacturing intelligence can promote the improvement of green innovation performance is that manufacturing intelligence is conducive to the production of "technology promotion effect" and "cost reduction effect", so as to promote green technology innovation, then effectively increase the desirable outputs and significantly decrease the undesirable outputs; The effect of manufacturing intelligence on green innovation performance has obvious regional heterogeneity: the improvement effect of manufacturing intelligence on green innovation performance in the eastern region is significantly higher than that in the central and western regions. In addition, further analysis shows that green technological progress rather than green technical efficiency is the main driving force for manufacturing intelligence to improve the dynamic green innovation performance.

Effects of Environmental Regulation on Green Total Factor Productivity: An Evidence from the Yellow River Basin, China

Based on the data of 59 prefecture-level cities in the Yellow River Basin from 2011 to 2019, this paper uses the Slack Based Measure-Global Malmquist Luenberger (SBM-GML) model to measure green total factor productivity (GTFP) of the cities. Under the space–time concept of the Basin, heterogeneity analysis of the upper, middle and lower reaches of the Yellow River Basin is conducted. On this basis, a panel Tobit model is constructed to analyze the impact of environmental regulation on GTFP in the whole basin, upstream region, middle region and downstream region. The results show that the intensity of environmental regulation in the Yellow River Basin increases gradually, which is the highest in the lower reaches, followed by the middle reaches; spatially, the intensity of environmental regulation shows a certain aggregation trend. The green economic growth is realized in the whole basin, and the green technology progress effect is the driving factor of GTFP. The GTFP distribution in the upstream region is relatively concentrated, showing a slow upward trend. The distribution of GTFP in the middle reaches is discrete, and the annual difference is large. In the downstream region, it shows a trend of decline first and then increase. Environmental regulation promotes GTFP in the whole basin, upper, middle and lower reaches, accompanied by certain spatial differences. The Yellow River Basin breaks through the cost effect brought by environmental regulation and triggers technological innovation, thereby enhancing GTFP; the “Porter hypothesis” has been verified in the Yellow River Basin.

Construction of Wireless Sensor Model for Carbon Neutralization and Environmental Protection from the Perspective of Energy Internet of Things Transformation

Carbon neutral environmental protection is an important way to effectively control the rapid rise of global temperature, promote the green transformation of energy utilization, and promote green, low-carbon, and other technological progress. It is a new driving force for world economic development and growth. This paper explores the construction of wireless sensor model for carbon neutralization and environmental protection from the perspective of the transformation of energy Internet of things. This paper analyzes the development background and research status of energy Internet of things technology at home and abroad and determines the overall design scheme of environmental monitoring Internet of things system. Then, we design the overall scheme of wireless sensor model under the environment monitoring Internet of things system. An experimental test platform was built to test the performance of the environmental monitoring Internet of things system. The test results show that the wireless sensor can monitor and respond to physical impact, and the generated energy can realize wireless data transmission of about 20 m; the regional coverage and fixed-point Internet of things system can realize the real-time monitoring of environmental parameters such as light, temperature, humidity, and dust, interact with the data and instructions of the cloud platform normally, and work stably. The basic functions of the whole Internet of things system have been realized, including the alarm function of passive wireless impact sensing, the cloud real-time monitoring function of environmental data and the real-time sending function of cloud instructions, and the real-time monitoring of indoor and outdoor environment under two working modes. The task objectives set in the early stage of the whole system are realized.

The spatiotemporal dynamic and spatial spillover effect of agricultural green technological progress in China.

The progress of agricultural green technology is an important means and fundamental way to achieve high-quality development of agriculture. The current study takes the panel data of 31 provinces in China from 1998 to 2018 and uses the Epsilon Based Measure-Global Malmquist-Luenberger (EBM-GML) model to measure China’s agricultural green technological progress (AGTP) and discusses its dynamic evolution characteristics in the spatiotemporal dimensions. Finally, we analyze the spatial spillover effects of AGTP by the spatial Dubin model. The results show that China’s AGTP showed a trend of first rising and then falling, and the average value is 1.0525. AGTP has obvious regional unbalanced development, and the regional differences are expanding. It shows that AGTP between adjacent areas is closely linked. The Moran’s I index shows that AGTP has a significant positive spatial correlation. The local Moran’s I index shows that AGTP is concentrated in Northwest, Northeast, and North China, and green technological is degraded in East and South China. From the spatial spillover effects of AGTP, the level of agricultural economic development, real GDP per capita, and urbanization have significantly promoted AGTP in the local and neighboring areas, while the agricultural internal structure and the level of labor inhibit AGTP in the local and neighboring areas. In addition, the administrative environmental policy (ENVP) and the economic environmental policy (ECOP) have negative impacts in neighboring areas, while the policy has negative spillover effects and positive spillover effects in the local area, respectively. Therefore, we should adhere to the concept of green development, pay attention to the regional exchange of green technology, concentrate policies on low-low concentration areas, and increase the follow-up tracking and supervision mechanism of the policy design and implementation process.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-021-18424-z.

Does Environmental Regulation Promote Industrial Green Technology Progress? Empirical Evidence from China with a Heterogeneity Analysis

The complex relationship between environmental regulation and green technology progress has always been a hot topic of research, especially in developing countries, where the impact of environmental regulation is important. Current research is mainly concerned with the impact of the single environmental regulation on technological progress and lacks study on the diversity of environmental regulations. The main purpose of this paper is to examine the heterogeneity of the effects of different types of environmental regulation on industrial green technology progress. As China’s scale of economy and pollution emissions are both large, and the government has also made great efforts in environmental regulation, this paper takes China as the example for analyses. We first use the EBM-GML method to measure the industrial green technology progress of 30 provinces in China from 2000 to 2018, and then apply the panel econometric model and threshold model to empirically investigate the influence of 3 types of environmental regulation. The results show that, first, the impacts of environmental regulation on industrial green technology progress are significantly different; specifically, command-based regulation has no direct significant impact, and autonomous regulation has played a positive role, and market-based regulation’s quadratic curve effect is significant, in which the cost-based and investment-based tool presents an inverted U-sharped and U-sharped, respectively. Second, there may be a weak alternative interaction among different types of environmental regulation. Third, a market-based regulatory tool has a threshold effect; with the upgrading of environmental regulation compliance, the effect of a cost-based tool is characterized by “promotion inhibition”, and that of an investment-based tool is “inhibition promotion”. Finally, the results of regional analysis are basically consistent with those of the national analysis. Based on the study, policy enlightenment is put forward to improve regional industrial green technology progress from the perspective of environmental regulation. This paper can provide a useful analytical framework for studying the relationship between environmental regulation and technological progress in a country, especially in developing countries.

Green technology progress and total factor productivity of resource-based enterprises: A perspective of technical compensation of environmental regulation

• Green technology progress promotes the total factor productivity of enterprises. • Environmental regulation promotes the green technology progress of enterprises. • The improvement of unit labor productivity is an influencing mechanism. • The push of external pressure is an influencing mechanism. The implementation of Industry 4.0 has had an important impact on the value creation of the manufacturing industry, but there are few studies on its driving mechanisms and effect on total factor productivity. In this study, green technology progress is selected as an important representation of Industry 4.0, and samples of China's A-share resource-based enterprises from 2004 to 2018 are used to examine the relationship between environmental regulation, green technology progress, and total factor productivity to explore the role of social, environmental, and technological factors in manufacturing development. The findings show that green technology progress significantly promotes the total factor productivity of enterprises through the improvement of unit labor productivity and that environmental regulation “forces” the green technology progress of enterprises through external pressure. These conclusions remain valid after replacing the explained variables and measuring methods. The results of heterogeneity analysis show that when an enterprise has strong financial and human resource bases, the “forcing” effect of environmental regulation on green technology progress is more significant.

Impact of low-carbon pilot cities on surrounding cities

Innovation is the source of strength for urban development, and LC is the only way for urban development. Affected by factors such as fragile ecology, single industrial structure, weak innovation foundation and geographical disadvantages, low-carbon (LC) pilot policies will hinder technological innovation in resource-based cities, large cities and western cities. The results show that the pilot policy of LC cities has significantly promoted the growth of urban green total factor productivity, but this effect shows typical heterogeneity in different regions; In the eastern pilot cities, it is significantly positive, while in the central and Western pilot cities, it is significantly negative. After decomposing urban green total factor productivity into urban green technological progress and technological efficiency, it is found that the pilot policy of LC city has a significant effect on urban technological efficiency and urban green technological progress. Accordingly, some suggestions are put forward to further promote the LC pilot policy and guide the construction of LC cities.

Retraction Note: Environmental regulation of PM2.5 in urban green vegetation and the influence of green technology progress based on remote sensing image

Retraction Note: Environmental regulation of PM2.5 in urban green vegetation and the influence of green technology progress based on remote sensing image

The Evolution of Factors Influencing Green Technological Progress in Terms of Carbon Reduction: A Spatial-Temporal Tactic Within Agriculture Industries of China

The impacts of widespread carbon emission trends possessed tremendous pressure for global food security, sustainable development, and ecosystems. Several temporal and spatial patterns of green technology have been adopted to reduce carbon emissions in different regions of China. In China, agriculture industries may have colossal importance for reducing carbon emissions. On the basis of the data from 1998 to 2018, the study uses the heterogeneous stochastic frontier model to quantify the carbon emission reduction potential of agricultural green technology progress in eastern, central, and western regions of China by using the heterogeneous stochastic frontier model. We also analyze the coefficient of variation and its spatial and temporal evolution pattern of carbon intensity decline potential index and explore the potential factors related to the agriculture green technology progress of China. The finding of the study revealed that the carbon emission rate in the agriculture industry of China is very high, whereas adopting green technology is slower because of economic and policy-related factors—the carbon emission of green technological progress. In terms of spatial variations, the changes in various regions were consistent with the overall fluctuating rate compared with the state of another country, but an increasing trend has been traced within the “east-central-west” regions. The overall regional differences are gradually trending, but differences between regions mainly cause them. The increase in the structure of the agricultural agriculture industry, the level of labor, and the increase in administrative environmental regulations will weaken the obstacles to the carbon emission reduction potential of green technological progress. The increase in urbanization, the level of the agricultural economy, and economic and environmental regulations will increase the carbon emission reduction potential of green technological progress. It is necessary to actively promote exchanges and cooperation in green agricultural technology and advanced management concepts, accelerate the optimization and upgrading of the industrial structure, and achieve the goal of peaking carbon emissions through regional coordinated development. Regionally, the overall external environment and the level of green technology progress in the western region need to be improved in all respects. The central and eastern regions need to focus on combining different policy tools to transform them from hindrance to promotion.

To Advance Industrial Green Technology via Environmental Governance—Evidence from China’s Industrial Sector

Industrial green technology progress is an effective way to realize high-quality economic development in China. The different competitive incentives of local governments make a difference in the intensity of environmental regulation between regions. The intensity of environmental regulation is different in different areas of the same industry, leading to the inter-regional transfer of pollution enterprises. The regional distribution of industries is different, which determines the different industries’ needs to coordinate different regions. Only when various industries realize the coordination of regional governance can they jointly promote the progress of industrial green technology. Based on data from 33 industrial sectors in China from 2001 to 2015 and considering the institutional evolution of governance synergy, this study comprehensively investigated the influence mechanism of local government environmental regulation on the industrial green technology progress using the mediating effect model. We found that environmental regulation promoted industrial technological progress through governance synergy and a low degree of inter-regional regulation coordination hindered the industries’ green technology progress. With the change in inter-regional governance synergy levels, we further discovered that the impact of environmental regulation on industrial green technology progress changed substantially. At a low level of governance synergy, environmental regulation inhibited industrial green technology progress. At a high level of governance synergy, environmental regulation promoted industrial green technology progress. While strengthening environmental regulation, we should promote inter-regional cooperation at the industry level. Only by collectively enforcing pollution regulations in industrial level can industrial green technology progress be promoted.

Addressing the severity and intensity of poverty in Sub-Saharan Africa: how relevant is the ICT and financial development pathway?

The study examines the effectiveness of ICT diffusion and financial development in reducing the severity and intensity of poverty in Sub-Saharan Africa (SSA). Using data from the World Development Indicators and the Global Consumption and Income Project (1980–2019), we provide evidence, robust to several specifications from the dynamic system GMM and the panel corrected standard errors estimation techniques, to show that, compared to financial access, ICT usage, and ICT access, ICT skills is remarkable in reducing both the severity and intensity of poverty. The results further revealed that, though ICT skills reduce poverty, the effect is more pronounced in the presence of enhanced financial development. Policy recommendations are provided in line with the region's green growth agenda and technological progress.