ORIGINAL RESEARCH
Driving Eco-Digital Transformation:
Exploring the Industrial and Digital Pathways
to Boost Carbon Emission Efficiency
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1
School of Public Administration, Hohai University, Nanjing, 211100, China
2
School of Language, Culture and Media, Hefei University, Hefei, 230000, China
3
Department of Economics, Division of Management and Administrative Science,
University of Education Lahore, Pakistan
Submission date: 2024-08-27
Final revision date: 2024-10-18
Acceptance date: 2024-11-10
Online publication date: 2025-01-29
Publication date: 2026-01-29
Corresponding author
Tifeng Liu
School of Public Administration, Hohai University, Nanjing, 211100, China
Pol. J. Environ. Stud. 2026;35(1):197-207
KEYWORDS
TOPICS
ABSTRACT
Carbon emissions mainly cause environmental devastation, and they’re continuously rising
in the atmosphere. It is responsible for global warming, which causes severe climatic events, including
high temperatures and uneven rain distribution. However, the increasing global population has generated
a high demand for sustainable energy production and consumption. Therefore, for sustainable economic
development, it is necessary to produce economic outputs with minimal environmental hazards.
This study measures the total factor carbon emissions (CEE), consisting of input and two types of output
(gross domestic output as the desired output and CO2 emissions as the undesired output), by utilizing
complete data from 181 different economies for the period 1995-2022. Subsequently, it explores the
dynamic relationship between the digital economy (DIGI), industrial structure (INDSI), population
density (PD), and carbon emissions through the lens of the environmental Kuznets curve (EKC). Four
econometric approaches were used to obtain robust findings to address the problems of heterogeneity,
autocorrelation, and endogeneity. The outcomes revealed a significant positive impact of DIGI
and INDSI and a negative impact of PD on carbon emissions. Moreover, INDSI significantly moderates
the relationship between DIGI and CEE, increasing the positive environmental externality of DIGI.
The findings also confirm the existence of the EKC, implying that CEE decreases with an increase
in economic growth. After a certain level of economic growth, the CEE also started to increase.
Therefore, both the DIGI and INDSI can significantly contribute to reducing carbon emissions, leading to
a high CEE. Economies may adopt the incentive and award system, promote R&D in the industrial sector
through the collaboration of academic and research institutions, and transform their structure along with
the adoption of digital technologies to achieve the efficient use of energy and resources.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
REFERENCES (59)
1.
KUMAR RAI D., SEN S. Investigation of the causality between participation in global value chains and CO2 emissions between developed and developing countries. The Journal of International Trade and Economic Development, 1, 2024.
https://doi.org/10.1080/096381....
2.
XIE Z., WU R., WANG S. How technological progress affects the carbon emission efficiency? Evidence from national panel quantile regression. Journal of Cleaner Production, 307, 127133, 2021.
https://doi.org/10.1016/j.jcle....
3.
JIAO J., CHEN C., BAI Y. Is green technology vertical spillovers more significant in mitigating carbon intensity? Evidence from Chinese industries. Journal of Cleaner Production, 257, 120354, 2020.
https://doi.org/10.1016/j.jcle... PMCid:PMC9699824.
4.
SHAH S.A., YE X., WANG B., WU X. Dynamic Linkages among Carbon Emissions, Artificial Intelligence, Economic Policy Uncertainty, and Renewable Energy Consumption: Evidence from East Asia and Pacific Countries. Energies, 17 (16), 4011, 2024.
https://doi.org/10.3390/en1716....
5.
WANG K., WU M., SUN Y., SHI X., SUN A., ZHANG P. Resource abundance, industrial structure, and regional carbon emissions efficiency in China. Resources Policy, 60, 203, 2019.
https://doi.org/10.1016/j.reso....
6.
SUN L., WU L., QI P. Global characteristics and trends of research on industrial structure and carbon emissions: a bibliometric analysis. Environmental Science and Pollution Research, 27, 44892, 2020.
https://doi.org/10.1007/s11356... PMid:32996091.
7.
LYU K., YANG S., ZHENG K., ZHANG Y. How does the digital economy affect carbon emission efficiency? Evidence from energy consumption and industrial value chain. Energies, 16 (2), 761, 2023.
https://doi.org/10.3390/en1602....
8.
WANG L., CHEN L., LI Y. Digital economy and urban low-carbon sustainable development: the role of innovation factor mobility in China. Environmental Science and Pollution Research, 29 (32), 48539, 2022.
https://doi.org/10.1007/s11356... PMid:35192162 PMCid:PMC9858329.
9.
YI M., LIU Y., SHENG M.S., WEN L. Effects of digital economy on carbon emission reduction: New evidence from China. Energy Policy, 171, 113271, 2022.
https://doi.org/10.1016/j.enpo....
10.
LI Z., WANG J. The dynamic impact of digital economy on carbon emission reduction: evidence city-level empirical data in China. Journal of Cleaner Production, 351, 131570, 2022.
https://doi.org/10.1016/j.jcle....
11.
DONG F., HU M., GAO Y., LIU Y., ZHU J., PAN Y. How does digital economy affect carbon emissions? Evidence from global 60 countries. Science of The Total Environment, 852, 158401, 2022.
https://doi.org/10.1016/j.scit... PMid:36057304.
12.
MIELNIK O., GOLDEMBERG J. Communication The evolution of the "carbonization index" in developing countries. Energy Policy, 27 (5), 307, 1999.
https://doi.org/10.1016/S0301-....
13.
SUN J.W. The decrease of CO2 emission intensity is decarbonization at national and global levels. Energy Policy, 33 (8), 975, 2005.
https://doi.org/10.1016/j.enpo....
14.
ZHANG Z., QU J., ZENG J. A quantitative comparison and analysis on the assessment indicators of greenhouse gases emission. Journal of Geographical Sciences, 18, 387, 2008.
https://doi.org/10.1007/s11442....
15.
HUANG J., LIU Q., CAI X., HAO Y., LEI H. The effect of technological factors on China's carbon intensity: new evidence from a panel threshold model. Energy Policy, 115, 32, 2018.
https://doi.org/10.1016/j.enpo....
16.
WANG S., FANG C., SUN L., SU Y., CHEN X., ZHOU C., FENG K., HUBACEK K. Decarbonizing China's urban agglomerations. Annals of the American Association of Geographers, 109 (1), 266, 2019.
https://doi.org/10.1080/246944....
17.
CHENG Z., LI L., LIU J., ZHANG H. Total-factor carbon emission efficiency of China's provincial industrial sector and its dynamic evolution. Renewable and Sustainable Energy Reviews, 94, 330, 2018.
https://doi.org/10.1016/j.rser....
19.
WANG Q., LIU S. How do FDI and technological innovation affect carbon emission efficiency in China? Energies, 15 (23), 9209, 2022.
https://doi.org/10.3390/en1523....
20.
HE A., XUE Q., ZHAO R., WANG D. Renewable energy technological innovation, market forces, and carbon emission efficiency. Science of the Total Environment, 796, 148908, 2021.
https://doi.org/10.1016/j.scit... PMid:34274672.
21.
SUN W., HUANG C. How does urbanization affect carbon emission efficiency? Evidence from China. Journal of Cleaner Production, 272, 122828, 2020.
https://doi.org/10.1016/j.jcle....
22.
PEI Y., ZHU Y., LIU S., WANG X., CAO J. Environmental regulation and carbon emission: The mediation effect of technical efficiency. Journal of Cleaner Production, 236, 117599, 2019.
https://doi.org/10.1016/j.jcle....
23.
ZHAO X., LONG L., YIN S., ZHOU Y. How technological innovation influences carbon emission efficiency for sustainable development? Evidence from China. Resources, Environment and Sustainability, 14, 100135, 2023.
https://doi.org/10.1016/j.rese... PMCid:PMC11006199.
24.
FAN G., ZHU A., XU H. Analysis of the impact of industrial structure upgrading and energy structure optimization on carbon emission reduction. Sustainability, 15 (4), 3489, 2023.
https://doi.org/10.3390/su1504....
25.
DONG J., HE J., LI X., MOU X., DONG Z. The effect of industrial structure change on carbon dioxide emissions: a cross-country panel analysis. Journal of Systems Science and Information, 8 (1), 1, 2020.
https://doi.org/10.21078/JSSI-....
26.
ZHAO X., XI Y. Threshold effects of urban population size and industrial structure on CO2 emissions in China. Frontiers in Environmental Science, 10, 894442, 2022.
https://doi.org/10.3389/fenvs.....
27.
HAN Y., ZHANG J., YUAN M. Carbon emissions and economic growth in the Yellow River Basin: Decoupling and driving factors. Frontiers in Environmental Science, 10, 1089517, 2022.
https://doi.org/10.3389/fenvs.....
28.
GAO P., WANG Y., ZOU Y., SU X., CHE X., YANG X. Green technology innovation and carbon emissions nexus in China: Does industrial structure upgrading matter? Frontiers in Psychology, 13, 951172, 2022.
https://doi.org/10.3389/fpsyg.... PMid:35959076 PMCid:PMC9362775.
29.
HU J., CHEN J., ZHU P., HAO S., WANG M., LI H., LIU N. Difference and cluster analysis on the carbon dioxide emissions in China during COVID-19 lockdown via a complex network model. Frontiers in Psychology, 12, 795142, 2022.
https://doi.org/10.3389/fpsyg.... PMid:35095680 PMCid:PMC8790068.
30.
CHEN H. Industrial production evaluation with the consideration of technology accumulation. Structural Change and Economic Dynamics, 62, 72, 2022.
https://doi.org/10.1016/j.stru....
31.
REHMAN S.U., KRAUS S., SHAH S.A., KHANIN D., MAHTO R.V. Analyzing the relationship between green innovation and environmental performance in large manufacturing firms. Technological Forecasting and Social Change, 163, 120481, 2021.
https://doi.org/10.1016/j.tech....
32.
KOOMEY J.G., SCOTT MATTHEWS H., WILLIAMS E. Smart everything: Will intelligent systems reduce resource use? Annual Review of Environment and Resources, 38 (1), 311, 2013.
https://doi.org/10.1146/annure....
33.
ARTS K., IORIS A.A., MACLEOD C.J., HAN X., SRIPADA S.G., BRAGA J.R., VAN DER WAL R. Environmental communication in the Information Age: Institutional barriers and opportunities in the provision of river data to the general public. Environmental Science and Policy, 55, 47, 2016.
https://doi.org/10.1016/j.envs....
34.
CARAGLIU A., NIJKAMP P. Space and knowledge spillovers in European regions: the impact of different forms of proximity on spatial knowledge diffusion. Journal of Economic Geography, 16 (3), 749, 2016.
https://doi.org/10.1093/jeg/lb....
35.
PÉREZ-TRUJILLO M., LACALLE-CALDERÓN M. The impact of knowledge diffusion on economic growth across countries. World Development, 132, 104995, 2020.
https://doi.org/10.1016/j.worl....
36.
YI M., WANG Y., SHENG M., SHARP B., ZHANG Y. Effects of heterogeneous technological progress on haze pollution: Evidence from China. Ecological Economics, 169, 106533, 2020.
https://doi.org/10.1016/j.ecol....
37.
WU H., XUE Y., HAO Y., REN S. How does internet development affect energy-saving and emission reduction? Evidence from China. Energy Economics, 103, 105577, 2021.
https://doi.org/10.1016/j.enec....
38.
SHAHBAZ M., WANG J., DONG K., ZHAO J. The impact of digital economy on energy transition across the globe: The mediating role of government governance. Renewable and Sustainable Energy Reviews, 166, 112620, 2022.
https://doi.org/10.1016/j.rser....
39.
CARDONA M., KRETSCHMER T., STROBEL T. ICT and productivity: conclusions from the empirical literature. Information Economics and Policy, 25 (3), 109, 2013.
https://doi.org/10.1016/j.info....
40.
TONE K. A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130 (3), 498, 2001.
https://doi.org/10.1016/S0377-....
42.
SULEMANA I., NKETIAH-AMPONSAH E., CODJOE E.A., ANDOH J.A.N. Urbanization and income inequality in Sub-Saharan Africa. Sustainable Cities and Society, 48, 101544, 2019.
https://doi.org/10.1016/j.scs.....
43.
ARELLANO M., BOND S. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58 (2), 277, 1991.
https://doi.org/10.2307/229796... PMCid:PMC11075896.
44.
CASELLI F., ESQUIVEL G., LEFORT F. Reopening the Convergence Debate: A New Look at Cross-Country Growth Empirics. Journal of Economic Growth, 1 (3), 363, 1996.
https://doi.org/10.1007/BF0014....
45.
RAMZAN M., HONGXING Y., ABBAS Q., FATIMA S. Do financial inclusion, inclusive digitalization and growth promote clean energy portfolio? Clean Technologies and Environmental Policy, 1, 2024.
https://doi.org/10.1007/s10098....
46.
KONSTANTAKOPOULOU I. Does health quality affect tourism? Evidence from system GMM estimates. Economic Analysis and Policy, 73, 425, 2022.
https://doi.org/10.1016/j.eap.....
47.
ZHOU G., GAO J., XU Y., ZHANG Y., KONG H. The Impact and Mechanism behind the Effect of a Digital Economy on Industrial Carbon Emission Reduction. Sustainability, 16 (13), 5705, 2024.
https://doi.org/10.3390/su1613....
48.
LIU H., LEI H., XIAO W., ZHAO S. Can the Digital Economy Achieve Low-Carbon Development? An Analysis Based on the Dual Perspectives of Reducing Emissions and Increasing Efficiency. Sustainability, 16 (14), 6198, 2024.
https://doi.org/10.3390/su1614....
49.
KIM J., PARK J.C., KOMAREK T. The impact of Mobile ICT on national productivity in developed and developing countries. Information and Management, 58 (3), 103442, 2021.
https://doi.org/10.1016/j.im.2....
50.
ANG J.H., GOH C., SALDIVAR A.A.F., LI Y. Energy-efficient through-life smart design, manufacturing and operation of ships in an industry 4.0 environment. Energies, 10 (5), 610, 2017.
https://doi.org/10.3390/en1005....
51.
REN S., HAO Y., XU L., WU H., BA N. Digitalization and energy: How does internet development affect China's energy consumption? Energy Economics, 98, 105220, 2021.
https://doi.org/10.1016/j.enec....
52.
SONG C., LIU Q., SOONG J., MA W. Impact path of digital economy on carbon emission efficiency: Mediating effect based on technological innovation. Journal of Environmental Management, 358, 120940, 2024.
https://doi.org/10.1016/j.jenv... PMid:38652994.
53.
ZHANG S., YU R., WEN Z., XU J., LIU P., ZHOU Y., ZHENG X., WANG L., HAO J. Impact of labor and energy allocation imbalance on carbon emission efficiency in China's industrial sectors. Renewable and Sustainable Energy Reviews, 184, 113586, 2023.
https://doi.org/10.1016/j.rser....
54.
JIANG M., AN H., GAO X. Adjusting the global industrial structure for minimizing global carbon emissions: A network-based multi-objective optimization approach. Science of the Total Environment, 829, 154653, 2022.
https://doi.org/10.1016/j.scit... PMid:35314220.
55.
FENG T., LIU B., WEI Y., XU Y., ZHENG H., NI Z., ZHU Y., FAN X., ZHOU Z. Research on the low-carbon path of regional industrial structure optimization. Energy Strategy Reviews, 54, 101485, 2024.
https://doi.org/10.1016/j.esr.....
56.
XUAN S., GE W., YANG P., ZHANG Y. Exploring digital finance, financial regulations and carbon emission nexus: New insight from resources efficiency, industrial structure and green innovation in China. Resources Policy, 88, 104452, 2024.
https://doi.org/10.1016/j.reso....
57.
YUE Q., LV S. Impact of Digital Transformation on Carbon Performance of Industrial Firms Considering Performance-Expectation Gap as a Moderator. Sustainability, 16 (14), 6097, 2024.
https://doi.org/10.3390/su1614....
58.
RAHMAN M.M. Do population density, economic growth, energy use and exports adversely affect environmental quality in Asian populous countries? Renewable and Sustainable Energy Reviews, 77, 506, 2017.
https://doi.org/10.1016/j.rser....
59.
MOHSIN M., ABBAS Q., ZHANG J., IKRAM M., IQBAL N. Integrated effect of energy consumption, economic development, and population growth on CO2 based environmental degradation: a case of transport sector. Environmental Science and Pollution Research, 26, 32824, 2019.
https://doi.org/10.1007/s11356... PMid:31502046.