Due to the growing worries of communities and governments about the depletion of natural resources and environmental contamination, green innovation (GI) is currently receiving much attention on a global scale. This study intends to investigate how Green Innovation Adoption (GIA) influences Sustainable Performance (SNP) in Saudi Arabia’s small- and medium-sized enterprises (SMEs) based on primary research. A conceptual framework model is developed to better comprehend the relationships of Government Support (GS), External Partnership and Cooperation (EPC), Rules and Regulatory Factors (RR), Market and Customer Factors (MC), Organization and Human Factors (OH), Green Innovation Strategy (GIS), and Technology Factors (TF) with GIA. The evaluation of hypotheses is performed using the Partial Least-Squares Structural Equation Modeling (PLS-SEM) method. The study’s findings are obtained using the SPSS 24.0 and AMOS 24.0 software programs. The results of this study reveal that GS, EPC, RR, MC, OH, and TF all have a positive impact on GIA. Furthermore, it has been noted that GIA has a positive impact on the economic, social, and environmental performance of SMEs in the Kingdom of Saudi Arabia. In accordance with the findings, corporate units that use GI would produce more acceptable eco-friendly and long-term performance.

Purpose: The present study is a novel attempt to measure the impact of population growth, natural resource depletion, non-renewable energy consumption, growth of national income, remittances inflow and industrial output on carbon dioxide emissions in India during the period of 1980–2018.Design/methodology/approach: Autoregressive distributive lag (ARDL) is used to achieve the objective. The application of FMOLS (fully modified ordinary least squares), DOLS (dynamic ordinary least squares) and CCR (canonical cointegrating regression) techniques illustrate statistical robustness.Findings: The long-run ARDL results confirm that increase in population, national income and energy consumption have a positive and significant impact on pollution levels in India. In contradiction to this, long run results further reveal that the increase in natural resource depletion, industrial output and remittances inflow have insignificant and negative impact on pollution levels in India. Further, the empirical findings did not find any evidence for the applicability of the environmental Kuznets curve (EKC) in India during the study period.Research limitations/implications: The study is confined to only a few important determinants of CO₂ emissions in India. However, there is a large chunk of studies that have incorporated other determinants of CO₂ emissions. Specifying a few determinants of CO₂ emissions in India is itself a lacuna in the present study. Moreover, taking the time period from 1980 to 2018 is also one of the limitations of the study.Practical implications: Plenty of research has been devoted to the causal relationship between the environment and its various determinants. However, not much attention has been paid to investigating the association between population growth, natural resource depletion, energy consumption, GDP per capita, remittances inflow, industry and carbon dioxide emissions in India. Since, CO₂ emissions are one of the widely accepted and applied emissions in EKC applications, which the present study intends to test. Moreover, the study employs advanced econometric techniques including ARDL framework, FMOLS, DOLS and CRR methodologies to achieve robust results. Such an investigation will potentially allow policymakers to frame efficient environmental and fiscal policies to achieve the desired results.Originality/value: The continuous increase of CO₂ emissions in India has compelled policy makers to prioritize this issue as soon as possible and formulate national environmental policy for reducing the share of carbon dioxides emissions in climate change. The study could constitute the focus of future research.