In this modern era, smart cities are an essential strategy for China to achieve high-quality development representing an essential issue at the stage of high-quality development to drive urban ...innovation and lead China to be among the innovative countries. In order to achieve this goal, this study employs a time-varying difference-in-difference (DID) model to test the above questions in a multidimensional manner based on micro-firm data from the China Tax Survey Database and the China Innovative Enterprise Database from 2010 to 2015. The estimated results explore that China's smart city pilot policies (SCPP) encourage green innovation at the firm level, a finding that still holds after a sequence of robustness tests. Furthermore, the empirical evidence indicates that the influence of green innovations on the SCPP was more important for firms in non-state-owned enterprises, heavily polluting industries and non-resource-based cities relative to state-owned firms, firms in lightly-polluting industries and firms in non-resource-based cities. In addition, this study finds that the smart city pilot policy can provide a new incentive for the green innovation behaviour of enterprises via policy and agglomeration effects. Overall, the estimated findings suggest that the government should continue to play a positive role in constructing smart cities for enterprises' green innovation and implement smart city construction programmes following local conditions to help China's high-quality economic development.
•The impact of Smart City pilot policies on the enterprises green innovations of micro-industrial enterprises is investigated•The method of time-varying difference-in-differences (DID) is applied•Firm, industry, and regional heterogeneity exist of the positive effect•We conducted a mechanism analysis from the perspective of the Policy effect and agglomeration effect
This study used the New Empirical Industrial Organization (NEIO) approach to estimate the presence and factors affecting the market power of the rice milling industry in Sri Lanka. Annual data from ...1982 to 2019 were used for the analysis. The miller’s pricing and demand models were estimated using the non-linear Seemingly Unrelated Regression (SUR) estimation technique. The estimated conjectural variation elasticity and the Lerner index of the Sri Lankan rice milling industry were 0.348 and 0.677 correspondingly and the results were significantly (p < 0.05) different from zero and one respectively. This depicts that the rice milling market exhibits oligopoly power in setting the price of rice above its marginal cost. Market power was significantly (p < 0.05) reduced by the implementation of the Government Ceiling Price (GCP) on rice. The study findings suggest the importance of consistent implementation and monitoring of GCP policies to control the market power of the rice milling industry in Sri Lanka.
JEL Classification: D22, D43, L00
This study integrates neoclassical economics and institutional theory to examine the relationship between market factor distortion, technical innovation, and China's electricity consumption, using ...business data from China (2008–2014). First, we provide evidence that factor market distortion promotes electricity consumption of industrial enterprises in China. Secondly, we divide technical innovation into traditional technical innovation and green innovation, finding that the level of technical innovation has an inverted U-shaped effect on the electricity consumption of industrial enterprises. Further, we also provide evidence that factor market distortion amplifies the inverted U-shaped effect of the technical innovation plot. The results show that accelerating the reform of the market, and promoting green innovation, should be regarded as important strategies for national energy saving and emission reduction.
To achieve the carbon peaking and neutrality targets in China, carbon price and technological innovation will play increasingly important roles in recent future. It is widely-known that carbon price ...and technological innovation can contribute significantly to emission reduction, respectively; but it is still unclear whether the cooperation effects of carbon price and technological innovation would be positive or negative. In this paper, we assume that there are 3 types of emission reduction measures in China's industrial enterprises, which are improvement of energy efficiency, adjustment of energy structure, and substitution of pollution inputs and non-pollution inputs; then we introduce carbon price and technological innovation respectively and simultaneously, and establish 12 scenarios based on the Data Envelopment Analysis models combined with material balance principal (DEA-MBP), and estimate the additional emission reductions and additional production costs of China's industrial enterprises when carbon price and technological innovation exist respectively or simultaneously. The counterfactually estimating results show that there would be significant regional and sectorial heterogeneities in carbon emission reduction characteristics for China's industrial enterprises. If low-carbon technologies in some sectors have the ability to reduce carbon emissions at the expense of high additional production cost, carbon pricing policies would encourage enterprises to adopt new mitigation technologies and increase additional emission reduction by more than 20%, especially technologies focusing on the adjustment of energy structure and the substitution of pollution inputs by non-pollution inputs. However, in some sectors which have already been covered by carbon pricing policies, the additional carbon pricing policy may not have a significant effect on emission reduction, and the emission reduction would decrease by 10%.
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•Cooperation effects of carbon price and technological innovation are estimated.•DEA-MBP models and multiple emission reduction measures/scenarios are applied.•Introduction of carbon price would encourage the adopting of new mitigation technologies.•Carbon price and technological innovation would have overlapping effects.•Significant regional and sectorial heterogeneities exist in emission reduction features.
Using the unique micro-data of industrial enterprises' Nitrogen oxides (NOx) and Sulfur oxides (SOx) emissions covering 58,725 enterprise-year observations, this paper investigates how digitalization ...affect energy efficiency. The empirical results show that regional digitalization can significantly promote the improvement of energy efficiency of industrial enterprises. Specifically, the result of PSM-DID method indicates that the impact of digitalization on energy efficiency is significantly enhanced by the “National innovative pilot cities” policy, while the dynamic result validates the continuity of industrial enterprises' energy efficiency. Moreover, regional digitalization has superior impact on enterprises within eastern region enterprises, the manufacturing sector, and with elevated liquidity and profitability levels. Finally, moderating effect suggests that technological innovation expand the positive impact of digitalization on enterprises’ energy efficiency. This study confirms regional digitalization can promote the energy efficiency of Chinese industrial enterprises, providing the policy suggestions to the energy transition in China.
•Measure the regional digitalization level of Prefecture-level city and the energy efficiency at Industrial enterprise-level.•Findings demonstrated that digitalization increases energy efficiency.•The impact of digitalization on energy efficiency can be enhanced by the “National innovative pilot cities” policy.•Technological innovation expands the positive impact of digitalization on enterprises' energy efficiency.
The problems of mining waste management at industrial enterprises and increasing the economy of production are among the most urgent ones, especially for the Arctic ecosystems, characterized by high ...sensitivity to external factors and the complexity of recovery. The research was conducted on the example of large industrial corporations directly operating in the Arctic, including PJSC PhosAgro, PJSC MMC Norilsk Nickel, Severstal Resource division of PJSC Severstal and PJSC NOVATEK. The key indicators related to waste management for 20182022 were analyzed. The trend of increasing volumes of mining waste in the Arctic was revealed, while the level of its utilization remained practically unchanged. It is proposed to intensify scientific research in the field of rational use of mining waste and to develop technologies for their processing, taking into account the impact on the Arctic ecosystems. It is suggested to create research and production centers for each industrial enterprise using the experience of the balanced “triple helix” model. It is recommended to actively participate in the Interdepartmental Scientific Council of the Russian Academy of Sciences on the development of the mineral resource base and its rational use, created in 2023, to develop the scientific foundations for the extraction and processing of minerals and accelerate import substitution.
An accurate and inexpensive preliminary risk assessment of industrial enterprise sites at a regional scale is critical for environmental management. In this study, we propose a novel framework for ...the preliminary risk assessment of industrial enterprise sites in the Yangtze River Delta, which is one of the fastest economic development and most prominent contaminated regions in China. Based on source–pathway–receptors, this framework integrated text and spatial analyses and machine learning, and its feasibility was validated with 8848 positive and negative samples with a calibration and validation set ratio of 8:2. The results indicated that the random forest performed well for risk assessment; and its accuracy, precision, recall, and F1 scores in the calibration set were all 1.0, and the four indicators for the validation set ranged from 0.97 to 0.98, which was better than that for the other models (e.g., logistic regression, support vector machine, and convolutional neural network). The preliminary risk ranking of industrial enterprise sites by the random forest showed that high risks (probabilities) were mainly distributed in Shanghai, southern Jiangsu, and northeastern Zhejiang from 2000 to 2015. The relative importance of the site industrial, production, and geographical features in the random forest was 69%, 22%, and 9%, respectively. Our study highlights that we could quickly and effectively establish a priority (or ranking) list of industrial enterprise sites that require further investigations, using the proposed framework, and identify potentially contaminated sites.
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•A novel framework for preliminary risk assessment of regional industrial enterprise sites.•The framework performed well and relative importance of the site industrial feature was 69%.•The framework established a priority list of the sites that require further investigations.•The framework is especially useful for areas where dense field sampling is not possible.
With the continuous development of technology, intelligent production, which brings huge changes to social welfare, has gradually become the main trend in the economic development of many countries. ...Does intelligent production featuring robot adoption help improve the environment and solve energy problems? In this study, we first construct a theoretical model at the micro-level to analyze how robot adoption affects a firm’s energy efficiency. Then, based on Propensity Score Matching-Difference in Differences (PSM-DID) method, we use the data of China’s firms from 2001 to 2012 to identify the causal relationship between robot adoption and firm’s energy efficiency. We find that the adoption of robots in production can significantly increase firms’ energy efficiency. Further mechanism tests show that the increase of productivity is an important factor through which adopting robots can improve a firm’s energy efficiency. In addition, the increase in the firm’s energy efficiency is mainly due to the increase in the firm’s output rather than the decrease in total energy consumption. Altogether, this study provides the first micro evidence on the relationship between robot adoption and energy efficiency, providing significant implications for the world’s sustainable development.
•Robot adoption affects firm’s energy efficiency and thus improve environment.•A theoretical model at micro-level analyzes the issue.•Based on PSM-DID, China’s firm-level data is used to identify causal relationship.•Improvement is due to increase in output, instead of decrease in energy consumption.
