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Carbon emissions are primarily the result of human activity in urban areas. Inadequate sanitary facilities, contaminated drinking water, nonrenewable energy, and high traffic congestion have all impacted the natural ecosystem. Using data from 1975 to 2019, the study assessed the impact of the aforementioned variables on Pakistan’s carbon emissions in light of this crucial fact. The ARDL cointegration method was used to estimate the short- and long-run parameter estimates. Urban sanitation challenges and energy consumption increase carbon emissions, which affects the natural environment by raising a country’s carbon intensity. Economic expansion confirmed the inverted U-shaped relationship between carbon emissions and economic growth to verify the Environmental Kuznets Curve (EKC) hypothesis in the long run. In contrast, the monotonically rising function of carbon emissions provides evidence of the nation’s economic development in the short run. Access to clean drinking water improves population health and encourages the purchase of eco-friendly products. The government must improve sanitation services and use renewable energy sources to enhance air quality.

Since human beings have a long tradition of coexistence with pandemics, which may profoundly impact them, adopting preventive measures is crucial for humankind’s survival. This study explores the intention-based critical factors affecting the willingness of individuals to adopt pandemic prevention. To this end, a representative sample of 931 Pakistanis filled in an online questionnaire. However, only 828 questionnaires were found to be complete and valid for path modeling analysis. The core findings are as follows: Firstly, peer groups’ beliefs, self-efficacy, perceived risk, pandemic knowledge, ease of pandemic prevention adoption, and risk-averse behavior are revealed as driving forces of the individuals’ willingness to adopt pandemic prevention. Contrastingly, a lack of trust in political will and mythical attitude towards pandemics are uncovered as inhibitors. Nevertheless, moral values depict a neutral role. Secondly, the peer groups’ beliefs are highest ranked, followed by the lack of trust in political will and a mythical attitude towards pandemic prevention. Finally, moral values are determined as the lowest-ranked critical factor. Based on these results, the government should promote awareness campaigns on lethality and fatality of the pandemic at both centralized and decentralized levels to win people’s trust at the grass-roots level and overcome the mythical attitude of individuals at all societal levels. Besides, access to personal protective gears should be made feasible since an easier pandemic prevention adoption would increase the individuals’ willingness to adopt such preventative measures.

Humanity is in more danger from escalating greenhouse gas (G.H.G.) emissions, making the world warmer. The study examined the relationship between China's environmental technologies, ecological innovation, and carbon emissions using time-series data from 1975 to 2020. The N.A.R.D.L. approach is used to examine the cointegration of variables in the short and long run. In the short run, environmental technologies, industrialisation (I.N.D.), positive shocks to natural resource depletion (N.R.D.), negative shocks to renewable energy (R.E.) use, and technical advancements affect carbon emissions. On the other hand, positive shocks to environmental technologies and financial development (F.D.), negative shocks to N.R.D., R.E. consumption (E.C.), and technical innovation all have a long-term effect on carbon emissions. Granger causality was used to examine the causal link between variables. According to the findings, environmental technologies, F.D., technical innovation, N.R.D., and economic growth (E.G.) cause carbon emissions. The impulse response function revealed an inverse link between asymmetric environmental technology and carbon emissions. In contrast, F.D. and N.R.D. directly affect environmental degradation over time. The outcome of the variance decomposition revealed that negative shocks of F.D. would likely exert greater pressure on achieving sustainable environmental agenda. Investment in environmental technology, F.D., technological innovation and R.E. should be encouraged by the Chinese government to achieve sustainable prosperity.

This study examines the impact of fossil fuel consumption, nonrenewable energy consumption, population, affluence, and poverty on carbon emissions in Pakistan by using a time series data from 1972 to 2014. The study uses a flexible ecological framework known as the STIRPAT model. The Auto Regressive Distributive Lag (ARDL) Model and Error Correction Model (ECM) are used to estimate the robust results. The results show that consumption of fossil fuels, population growth, improvement in affluence level, and urbanization are contributing factors to high carbon emissions in Pakistan. The results also highlight that poverty alleviation and carbon emissions have opposite trends, this shows that the efforts to reduce poverty are stimulating the consumption of low-cost energy sources such as fossil fuels, and contributing to carbon emissions. However, results indicate that an increase in the share of renewable energy in total energy use and consumption of hydroelectric energy has the potential to reduce carbon emissions in Pakistan. The results highlight that there is a need to promote the use of renewable and hydroelectric energy. At domestic level, this will assist to meet the energy demand of the growing population and also prove helpful to reduce carbon emissions. Thus, the study recommends that a transition from fossil fuel energy to renewable and hydroelectric energy could prove an effective strategy to improve the affluence level, to alleviate poverty and effective to reduce carbon emissions in Pakistan.

There are numerous anticipated effects of climate change (CC) on agriculture in the developing and the developed world. Pakistan is among the top ten most prone nations to CC in the world. The objective of this analysis was to quantify the economic impacts of CC on the agricultural production system and to quantify the impacts of suggested adaptation strategies at the farm level. The study was conducted in the Punjab province’s rice-wheat cropping system. For this purpose, climate modeling was carried out by using two representative concentration pathways (RCPs), i.e., RCPs 4.5 and 8.5, and five global circulation models (GCMs). The crop modeling was carried out by using the Agricultural Production Systems Simulator (APSIM) and the Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation models (CSMs), which were tested on the cross-sectional data of 217 farm households collected from the seven strata in the study area. The socio-economic impacts were calculated using the Multidimensional Impact Assessment Tradeoff Analysis Model (TOA-MD). The results revealed that CC’s net economic impact using both RCPs and CSMs was negative. In both CSMs, the poverty status was higher in RCP 8.5 than in RCP 4.5. The adaptation package showed positive results in poverty reduction and improvement in the livelihood conditions of the agricultural households. The adoption rate for DSSAT was about 78%, and for APSIM, it was about 68%. The adaptation benefits observed in DSSAT were higher than in APSIM. The results showed that the suggested adaptations could have a significant impact on the resilience of the atmospheric changes. Therefore, without these adaptation measures, i.e., increase in sowing density, improved cultivars, increase in nitrogen use, and fertigation, there would be negative impacts of CC that would capitalize on livelihood and food security in the study area.

Over the last few decades, economic policy uncertainty (EPU) has surged across the globe. Furthermore, EPU affects economic activities, which may also generate strong CO 2 emissions. The goal of this study is to explore the impact of EPU (measured by the world uncertainty index) on CO 2 emissions in the case of the top ten carbon emitter countries, spanning the period 1990 to 2015. The findings from the PMG-ARDL modelling approach document that the world uncertainty index (WUI) affects CO 2 emissions in both the short and the long run. In the short run, a 1% increase in WUI mitigates CO 2 emissions by 0.11%, while a 1% rise in WUI escalates CO 2 emissions by 0.12% in the long run. The findings could have some substantial practical effects on economic policies through which policy makers try to shrink any uncertainty by organizing and participating in international summits and treaties. In addition, international organizations could also launch certain programs to shrink uncertainties associated with economic policy. Finally, these countries should introduce innovation, renewable energy, and enforce alternative technologies that are environment friendly. Overall, governments must provide strong tax exemptions on the use of clean energy, while R&D budgets should also expand.

The study aims to analyze the economic determinants of national carbon emissions in a large cross-section of 119 countries. The study followed the 'theory of sustainable development' to assess the national sustainable developmental agenda. The study employed cross-sectional, robust least squares, and Markov switching regression for parameter estimates. The findings indicate that information disclosure, the cost of business start-up procedures, sustainable fuel imports, and renewable energy decrease emissions stock. In contrast, ease of doing business and logistics operations increase it. According to the ex-ante analysis, information disclosure, the cost of business start-up procedures, and environmentally friendly logistical operations would likely reduce emissions stock. Ease of doing business and lower renewable fuel expenditures will almost certainly increase emissions stock in the majority of subsequent years. Over time, information disclosure is expected to significantly impact carbon emissions, followed by renewable energy consumption, doing business, and logistical operations. Sustainable economic policies worldwide make it possible for green technology and environmentally friendly manufacturing to be put into place.

Since the turn of twenty first century, economic policy uncertainty (EPU) and geopolitical risk (GPR) have escalated across the globe. These two factors have both economic and environmental impacts. However, there exists dearth of literature that expounds the impact of EPU and GPR on environmental degradation. This study, therefore, probes the impact of EPU and GPR on ecological footprint (proxy for environmental degradation) in selected emerging economies. Cross-sectional dependence test, slope heterogeneity test, Westerlund co-integration test, fully modified least ordinary least square estimator, dynamic OLS estimator, and augmented mean group estimator are employed to conduct the robust analyses. The findings reveal that EPU and non-renewable energy consumption escalate ecological footprint, whereas GPR and renewable energy plunge ecological footprint. In addition, findings from the causality test reveal both uni-directional and bi-directional causality between a few variables. Based on the findings, we deduce several policy implications to accomplish the sustainable development goals in emerging economies.

This study attempts to analyse the influence of urbanization, economic growth, and population size on residential carbon emissions in the South Asian Association for Regional Cooperation (SAARC) member nations for the period 1994 to 2013. To do so, we employ an augmented STIRPAT model in an ecological analytic framework with a fixed effect regression model that incorporates Driscoll and Kraay standard errors. The results show that population size and per capita GDP are leading drivers of high carbon emissions in the SAARC countries. The empirical results show that a U-shaped relationship exists between urbanization and residential carbon emissions: residential carbon emissions initially decrease with an increase in urbanization, reach a turning point at 25.33% and then increase with urbanization. Thus, our findings suggest a need for stronger national policies to control the impact of urbanization and population growth on residential carbon emissions in the SAARC member countries.Graphic abstract

PurposeThis study proposed a model based on knowledge management infrastructure capabilities (KMICs) to explore that how functional flexibility (FF) and knowledge sharing (KS) plays a mediating role between KMIC and innovative work behavior (IWB) among production-line workers.Design/methodology/approachData were collected from 894 workers through self-administered questionnaires from manufacturing businesses. The outcomes of the study revealed that KMIC has a significant and positive relationship with IWB. Second, FF and KS fully mediate between KMIC and IWB. Finally, the moderating role of KS between KMIC and FF was also supported by this study.FindingsThis study offers the empirical evidence on how to flourish the mechanism of FF and KS in manufacturing concerns. Additionally, some facts are also highlighted by this study that should be eloquent options for HR managers to understand the nexus of KMIC, KS and FF for enhancing the IWB among production-line workers. Moreover, this study contributed to noteworthy inferences for management by focusing on KMIC as a fundamental organizational level factor for IWB via KS and FF.Originality/valueThis is the first study in the which emphasis on KMIC for explaining IWB through employee FF and KS in settings such as manufacturing concern where the implementation of new working methods is necessary.