Rapid economic expansion across Asia has exacerbated environmental challenges, raising concerns about balancing economic growth with sustainability. Addressing this dilemma, the study investigates the impact of technology usage and innovation on environmental and economic efficiency in 30 Asian economies. Using Data Envelopment Analysis (DEA), efficiency scores are calculated and integrated into an Environmental Kuznets Curve (EKC) model. The findings show that while technological advancements improve economic efficiency, their environmental benefits appear only after surpassing a critical economic threshold, estimated at approximately $14,900 per capita. Additionally, achieving high economic efficiency alone does not guarantee environmental sustainability unless innovation strategies specifically align with green growth. This study contributes to sustainable development literature by emphasizing innovation-driven policies necessary for balancing economic growth with environmental protection, providing policymakers with empirical guidance for technology-based sustainability strategies.
ContiniDCostabileF. Does air pollution influence COVID-19 outbreaks?Atmosphere (Basel)2020; 11: 377.
4.
ZhuYXieJHuangF, et al.Association between short-term exposure to air pollution and COVID-19 infection: evidence from China. Sci Total Environ2020; 727: 138704.
5.
LeeCTLimJSFanYV, et al.Enabling low-carbon emissions for sustainable development in Asia and beyond. J Cleaner Prod2018; 176: 726–735.
6.
DembekA. Innovation and sustainability united to solve global problems. Res-Technol Manag2020; 63: 60–60.
FäreRGrosskopfSTytecaD. An activity analysis model of the environmental performance of firms—application to fossil-fuel-fired electric utilities. Ecol Econ1996; 18: 161–175.
9.
JaffeABNewellRGStavinsRN. Environmental policy and technological change. Environ Resour Econ2002; 22: 41–70.
10.
PorterMELindeCVD. Toward a new conception of the environment-competitiveness relationship. J Econ Perspect1995; 9: 97–118.
11.
HorbachJRammerCRenningsK. Determinants of eco-innovations by type of environmental impact — the role of regulatory push/pull, technology push and market pull. Ecol Econ2012; 78: 112–122.
GrossmanGMHelpmanE. Innovation and growth in the global economy. 7. print. Cambridge, Mass.: MIT Press, 2001.
14.
BormotovM. Modern Knowledge Based Economy: All-Factors Endogenous Growth Model and Total Investment Allocation. MPRA Working Paper 19932, Munich Personal RePEc Archive: Canadian Economics Association.
15.
OECD. Towards Green Growth. OECD. Epub ahead of print 25 May 2011. DOI: 10.1787/9789264111318-en.
16.
GandaF. The impact of innovation and technology investments on carbon emissions in selected organisation for economic co-operation and development countries. J Cleaner Prod2019; 217: 469–483.
17.
ChenMSinhaAHuK, et al.Impact of technological innovation on energy efficiency in industry 4.0 era: moderation of shadow economy in sustainable development. Technol Forecast Soc Change2021; 164: 120521.
18.
ShanSGençSYKamranHW, et al.Role of green technology innovation and renewable energy in carbon neutrality: a sustainable investigation from Turkey. J Environ Manag2021; 294: 113004.
19.
AmbecSCohenMAElgieS, et al.The porter hypothesis at 20: can environmental regulation enhance innovation and competitiveness?Rev Environ Econ Policy2013; 7: 2–22.
SternDI. The rise and fall of the environmental Kuznets curve. World Dev2004; 32: 1419–1439.
25.
ChenKWupurALiuX, et al.Measuring multiple-path technology transfer efficiency in Chinese Universities: a network DEA-tobit approach. J Knowl Econ2025; 16: 1464–1493.
26.
CharnesACooperWWRhodesE. Measuring the efficiency of decision making units. Eur J Oper Res1978; 2: 429–444.
27.
BankerRDCharnesACooperWW. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage Sci1984; 30: 1078–1092.
28.
MiragaiaDAMFerreiraJJMVieiraCT. Efficiency of non-profit organisations: a DEA analysis in support of strategic decision-making. J Knowl Econ2024; 15: 3239–3265.
29.
AndersenPPetersenNC. A procedure for ranking efficient units in data envelopment analysis. Manage Sci1993; 39: 1261–1264.
30.
ChenY. Measuring super-efficiency in DEA in the presence of infeasibility. Eur J Oper Res2005; 161: 545–551.
31.
EmrouznejadAYangG. A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socioecon Plann Sci2018; 61: 4–8.
32.
LiuJSLuLYYLuW-M, et al.A survey of DEA applications. Omega (Westport)2013; 41: 893–902.
33.
ChangY-T. Environmental efficiency of ports: a data envelopment analysis approach. Marit Policy Manag2013; 40: 467–478.
34.
WangYWenZCaoX, et al.Environmental efficiency evaluation of China’s iron and steel industry: a process-level data envelopment analysis. Sci Total Environ2020; 707: 135903.
35.
UllahAPerretSR. Technical- and environmental-efficiency analysis of irrigated cotton-cropping systems in Punjab, Pakistan using data envelopment analysis. Environ Manag2014; 54: 288–300.
36.
DirikCŞahinSEnginP. Environmental efficiency evaluation of Turkish cement industry: an application of data envelopment analysis. Energy Effic2019; 12: 2079–2098.
37.
Hermoso-OrzáezMJGarcía-AlguacilMTerrados-CepedaJ, et al.Measurement of environmental efficiency in the countries of the European union with the enhanced data envelopment analysis method (DEA) during the period 2005–2012. Proc AMIA Annu Fall Symp2020; 38: 20.
38.
IramRZhangJErdoganS, et al.Economics of energy and environmental efficiency: evidence from OECD countries. Environ Sci Pollut Res2020; 27: 3858–3870.
39.
WangC-NNguyenH-PChangC-W. Environmental efficiency evaluation in the top Asian economies: an application of DEA. Mathematics2021; 9: 889.
40.
TwumFALongXSalmanM, et al.The influence of technological innovation and human capital on environmental efficiency among different regions in Asia-pacific. Environ Sci Pollut Res2021; 28: 17119–17131.
41.
YuDHeX. A bibliometric study for DEA applied to energy efficiency: trends and future challenges. Appl Energy2020; 268: 115048.
42.
SuzukiSNijkampP. High urban population density as a facilitator of energy –environment–economy performance– development of an autoconfiguration target model in data envelopment analysis. Asia-Pac J Reg Sci2021; 5: 261–287.
43.
ChaudhryADudejaGChandni. Total factor efficiency of energy and carbon emissions in 18 Asian countries: an empirical assessment. Jaes2023; 7: 77–90.
44.
KhanDUllahA. Comparative analysis of the technical and environmental efficiency of the agricultural sector: the case of Southeast Asia countries. Custos E Agronegocio Line2020; 16: 2–28.
45.
KuznetsS. Economic growth and income inequality. Am Econ Rev1955; 45: 1–28.
SeldenTMSongD. Environmental quality and development: is there a Kuznets curve for air pollution emissions?J Environ Econ Manage1994; 27: 147–162.
48.
SternDICommonMS. Is there an environmental Kuznets curve for Sulfur?J Environ Econ Manage2001; 41: 162–178.
49.
RocaJ. Do individual preferences explain the environmental Kuznets curve?Ecol Econ2003; 45: 3–10.
50.
BeedeDNBloomDEWheelerD. Measuring and explaining cross-establishment variation in the generation and management of industrial waste. World Bank Discussion paper, Presented to the American Economic Association, Anaheim, CA, USA, 1993.
51.
HettigeHManiMWheelerD. Industrial pollution in economic development: the environmental Kuznets curve revisited. J Dev Econ2000; 62: 445–476.
52.
KoopGToleL. Is there an environmental Kuznets curve for deforestation?J Dev Econ1999; 58: 231–244.
53.
BhattaraiMHammigM. Governance, economic policy, and the environmental Kuznets curve for natural tropical forests. Environ Dev Econ2004; 9: 367–382.
54.
BulteEHvan SoestDP. Environmental degradation in developing countries: households and the (reverse) environmental Kuznets curve. J Dev Econ2001; 65: 225–235.
55.
TuckerM. Carbon dioxide emissions and global GDP. Ecol Econ1995; 15: 215–223.
56.
World Bank. Enterprise surveys indicator descriptions. In: Indicator descriptions. Washington, DC: World Bank's Enterprise Analysis Unit, 2024.
57.
ChengCSASunWYeK, et al.The effect of auditing on promoting exports: evidence from private firms in emerging markets. Manage Sci2020; 66: 1692–1716.
58.
ChuLK. The effects of financial development on economic sophistication: evidence from panel data. Appl Econ Lett2020; 27: 1260–1263.
59.
RiazKSiddiqueHMAAudiM, et al.Impact of Financial Development on Export Performance: Evidence from South Asia. MPRA Paper 122474, Munich: Munich Personal RePEc Archive, https://mpra.ub.uni-muenchen.de/122474/ (2024).
60.
SpataforaNAnandRMishraS. Structural transformation and the sophistication of production. Washington, D.C: International Monetary Fund, 2012. Epub ahead of print 2012. DOI: 10.5089/9781463937775.001.
61.
MannHBWhitneyDR. On a test of whether one of two random variables is stochastically larger than the other. Ann Math Statist1947; 18: 50–60.
62.
HabibAMMouradN. Analyzing the efficiency of intellectual capital: a new approach based on DEA-MPI technology. BIJ2024; 31: 3860–3883.
63.
HinkleyDV. Jackknifing in unbalanced situations. Technometrics1977; 19: 285–292.
64.
WhiteH. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica1980; 48: 817.
