Globally, the interaction and vulnerability of tourism and climate change have recently been in focus. This study examines how carbon dioxide emissions respond to changes in the tourism development. Panel data from 2000 to 2017 for 70 countries are analyzed using spatial econometric method to investigate the spatial spillover effect of tourism development on environmental pollution. The direct, indirect, and overall impact of tourism on environmental pollution are estimated after the selection of the most appropriate GNS method. The findings reveal that tourism has a positive direct effect and a negative indirect effect; both are significant at the 1 % level. The negative indirect effect of tourism is greater than its direct positive effect, implying an overall significantly negative impact. Further, the outcome of financial development and carbon emissions have an inverted U-shaped and U-shaped relationship in direct and indirect impacts. Population density, trade openness and economic growth significantly influence on environmental pollution through spatial spill over. In addition, education expenditure and infrastructure play a significant moderating role in the relationship among tourism development and environmental pollution. The results have important policy implications as they establish an inverted-U-shaped relationship among tourism and environmental pollution and indicate that while a country's emissions initially rise with the tourism industry's growth, they begin declining after a limit.
Environmental degradation is significantly studied both in the past and the current literature; however, steps towards reducing the environmental pollution in carbon emission and haze pollution like PM2.5 are not under rational attention. This study tries to cover this gap while considering the carbon emission and PM2.5 through observing the role of renewable energy, non-renewable energy, environmental taxes, and ecological innovation for the top Asian economies from 1990 to 2017. For analysis purposes, this research considers cross-sectional dependence analysis, unit root test with and without structural break (Pesaran, 2007), slope heterogeneity analysis, Westerlund and Edgerton (2008) panel cointegration analysis, Banerjee and Carrion-i-Silvestre (2017) cointegration analysis, long-short run CS-ARDL results, as well as AMG and CCEMG for robustness check. The empirical evidence in both the short- and long-run has confirmed the negative and significant effect of renewable energy sources, ecological innovation, and environmental taxes on carbon emissions and PM2.5. Whereas, non-renewable energy sources are causing environmental degradation in the targeted economies. Finally, various policy implications related to carbon emission and haze pollution like PM2.5 are also provided to control their harmful effect on the natural environment.
The snow leopard (Panthera uncia) inhabits one of the most challenging environments on Earth, referred to as the 'third pole'. Only a fraction of its vast range has been explored thus far, owing to myriad of barriers inflicted by the remote terrain and socio-ecological realities of the landscapes. Understanding distribution patterns of species is essential to devise practical management measures. This study aimed to understand the distribution pattern and factors influencing occupancy of snow leopard in the Pamir Mountain range through sign-based occupancy modelling. Our study confirmed that the Pamir range is a snow leopard stronghold, with occupancy estimated at 0.57 ± 0.02. The topographic features positively influenced the detection probability (p = 0.37 ± 0.005) of snow leopards. Occupancy was influenced by mean annual precipitation (β = -6.12 ± 1.8), density of roads (β = -1.61 ± 0.6) and water sources (β = 0.74 ± 0.4). Our findings underpin that sign-based distribution surveys provide vigorous scientific knowledge about elusive species and merit replication being used for other species. We propose to redefine the protected area boundaries based on ecological knowledge and encourage transboundary cooperation to safeguard snow leopards at a landscape scale.
This paper investigates the efficiency and total factor productivity (TFP) growth of the Pakistani banking industry and determines the impact of risk and competition on the efficiency and TFP growth. The data envelopment analysis (DEA)-based Malmquist productivity index is used to measure efficiency and TFP growth of the Pakistani banking industry. The generalized method of moments (GMM) model is applied to observe the impact of risk and competition on efficiency and TFP growth. The motivation behind the use of GMM model is its ability to overcome unobserved heterogeneity, autocorrelation, and endogeneity issues. The results of the study show that the credit and liquidity risks have positive while insolvency risk has negative effect on the efficiency and TFP growth. The competition leads to improve technological efficiency but declines the technical efficiency growth. Among other explanatory variables, operational cost management, banking sector development, GDP growth rate, and infrastructure development show significant relationships with various efficiencies and TFP growth. The banks also facilitate for the purchase of carbon-intensive products in order to reduce carbon emissions. Strong banking development successfully allocate their financial resources for the development of energy-efficient technology while banking sector development is found to be negatively related with environmental sustainability. The strong banking sector possesses a significant negative influence on carbon reduction and environmental degradation.
Recent research has shown a huge impact of non-renewable energy (NRE) production on environmental health. In this context, this work analyzes the effects of GDP growth and long- and short-term consumption of renewable and non-renewable energy (RE and NRE, respectively) on carbon emission in BRICS and OECD economies. The quantile autoregressive distributed lag (QARDL) model was employed on the panel data from 1980 to 2016. Findings suggest a negative GDP-carbon emission correlation and a positive NRE-carbon emission correlation in the considered economies. Furthermore, carbon emission decreases with increase in gross capital formation, whereas trade openness does not have any significant effect on carbon emission. It has been determined that the application of the error correction method (ECM) has less effect on energy consumption as compared to the past levels and changes in energy consumption. In the long-term, a positive correlation of carbon emission and energy consumption is observed, whereas limited short-term effects of energy consumption on carbon emission are observed. Therefore, an RE-based energy production approach is recommended in the selected region for the future projects.
Green finance is inextricably linked to investment risk, particularly in emerging and developing economies (EMDE). This study uses the difference in differences (DID) method to evaluate the mean causal effects of a treatment on an outcome of the determinants of scaling up green financing and climate change mitigation in the N-11 countries from 2005 to 2019. After analyzing with a dummy for the treated countries, it was confirmed that the outcome covariates: rescon (renewable energy sources consumption), population, FDI, CO2, inflation, technical corporation grants, domestic credit to the private sector, and research and development are very significant in promoting green financing and climate change mitigation in the study countries. The probit regression results give a different outcome, as rescon, FID, CO2, Human Development Index (HDI), and investment in the energy sector by the private sector that will likely have an impact on the green financing and climate change mitigation of the study countries. Furthermore, after matching the analysis through the nearest neighbor matching, kernel matching, and radius matching, it produced mixed results for both the treated and the untreated countries. Either group experienced an improvement in green financing and climate change mitigation or a decrease. Overall, the DID showed no significant difference among the countries.
Over the last three decades, the world has been facing the phenomenon of the ecological deficit as the ecological footprint is continuously rising due to the persistent decline of the per-capita bio-capacity. Moreover, there is a substantial increase in globalization and electricity consumption for the same period, and transportation is contributing to economic prosperity at the cost of environmental sustainability. Understanding the determinants of ecological footprint is thus critical for suggesting appropriate policies for environmental sustainability. As a result, this study analyzes the impacts of economic globalization, transportation, coal rents, and electricity consumption in ecological footprint in the context of the USA over the period 1995 to 2018. The data have been extracted from "Global Footprint Network," "Swiss Economic Institute," and "World Development Indicators." The current study has also applied the flexible Fourier form nonlinear unit root test to examine the stationarity among variables. For the empirical estimation, a novel technique, the "quantile auto-regressive distributive lag model," is applied in the study to deal with the nonlinear associations of the variables and to evaluate the long-term stability of variables across quantiles. The study's findings indicate that coal rents, transportation, and globalization significantly and positively contribute to the deterioration of ecological footprints at different quantile ranges in the short and long run. Electricity consumption is found to have a positive and significant impact at lower quantile ranges in the long run but not have a significant impact in the short run. The study suggested that lowering the dependence of the transport sector on fossil fuels, more use of hydroelectricity, and stringent strategies to curb coal consumption would be helpful to reduce the positive influence of these variables on ecological footprints in the USA.
Although neglected, the mite Sarcoptes scabiei is an unpredictable emerging parasite, threatening human and animal health globally. In this paper we report the first fatal outbreak of sarcoptic mange in the endangered Himalayan lynx (Lynx lynx isabellinus) from Pakistan. A 10-year-old male Himalayan lynx was found in a miserable condition with severe crusted lesions in Chitral District, and immediately died. Post-mortem examination determined high S. scabiei density (1309 mites/cm(2) skin). It is most probably a genuine emergence, resulting from a new incidence due to the host-taxon derived or prey-to-predator cross-infestation hypotheses, and less probable to be apparent emergence resulting from increased infection in the Himalayan lynx population. This is an alarming situation for the conservation of this already threatened population, which demands surveillance for early detection and eventually rescue and treatment of the affected Himalayan lynx.
This work aims to study the time-frequency relationship between the recent COVID-19 pandemic and instabilities in oil price and the stock market, geopolitical risks, and uncertainty in the economic policy in the USA, Europe, and China. The coherence wavelet method and the wavelet-based Granger causality tests are applied to the data (31st December 2019 to 1st August 2020) based on daily COVID-19 observations, oil prices, US-EPU, the US geopolitical risk index, and the US stock price index. The short- and long-term COVID-19 consequences are depicted differently and may initially be viewed as an economic crisis. The results illustrate the reduced industrial productivity, which intensifies with the increase in the pandemic's severeness (i.e., a 10.57% decrease in the productivity index with a 1% increase in the pandemic severeness). Similarly, indices for oil demand, stock market, GDP growth, and electricity demand decrease significantly with an increase in the pandemic severeness index (i.e., a 1% increase in the pandemic severeness results in a 0.9%, 0.67%, 1.12%, and 0.65% decrease, respectively). However, the oil market shows low co-movement with the stock exchange, exchange rate, and gold markets. Therefore, investors and the government are recommended to invest in the oil market to generate revenue during the sanctions period.
Habitat suitability models are useful to understand species distribution and to guide management and conservation strategies. The grey wolf (Canis lupus) has been extirpated from most of its historic range in Pakistan primarily due to its impact on livestock and livelihoods. We used non-invasive survey data from camera traps and genetic sampling to develop a habitat suitability model for C. lupus in northern Pakistan and to explore the extent of connectivity among populations. We detected suitable habitat of grey wolf using a maximum entropy approach (Maxent ver. 3.4.0) and identified suitable movement corridors using the Circuitscape 4.0 tool. Our model showed high levels of predictive performances, as seen from the values of area under curve (0.971±0.002) and true skill statistics (0.886±0.021). The main predictors for habitat suitability for C. lupus were distances to road, mean temperature of the wettest quarter and distance to river. The model predicted ca. 23,129 km2 of suitable areas for wolf in Pakistan, with much of suitable habitat in remote and inaccessible areas that appeared to be well connected through vulnerable movement corridors. These movement corridors suggest that potentially the wolf range can expand in Pakistan's Northern Areas. However, managing protected areas with stringent restrictions is challenging in northern Pakistan, in part due to heavy dependence of people on natural resources. The habitat suitability map provided by this study can inform future management strategies by helping authorities to identify key conservation areas.
Pakistan's total estimated snow leopard habitat is about 80,000 km2 of which about half is considered prime habitat. However, this preliminary demarcation was not always in close agreement with the actual distribution-the discrepancy may be huge at the local and regional level. Recent technological developments like camera trapping and molecular genetics allow for collecting reliable presence records that could be used to construct realistic species distribution based on empirical data and advanced mathematical approaches like MaxEnt. The current study followed this approach to construct an accurate distribution of the species in Pakistan. Moreover, movement corridors, among different landscapes, were also identified through circuit theory. The probability of habitat suitability, generated from 98 presence points and 11 environmental variables, scored the snow leopard's assumed range in Pakistan, from 0 to 0.97. A large portion of the known range represented low-quality habitat, including areas in lower Chitral, Swat, Astore, and Kashmir. Conversely, Khunjerab, Misgar, Chapursan, Qurumber, Broghil, and Central Karakoram represented high-quality habitats. Variables with higher contributions in the MaxEnt model were precipitation during the driest month (34%), annual mean temperature (19.5%), mean diurnal range of temperature (9.8%), annual precipitation (9.4%), and river density (9.2). The model was validated through receiver operating characteristic (ROC) plots and defined thresholds. The average test AUC in Maxent for the replicate runs was 0.933 while the value of AUC by ROC curve calculated at 0.15 threshold was 1.00. These validation tests suggested a good model fit and strong predictive power. The connectivity analysis revealed that the population in the Hindukush landscape appears to be more connected with the population in Afghanistan as compared to other populations in Pakistan. Similarly, the Pamir-Karakoram population is better connected with China and Tajikistan, while the Himalayan population was connected with the population in India. Based on our findings we propose three model landscapes to be considered under the Global Snow Leopard Ecosystem Protection Program (GSLEP) agenda as regional priority areas, to safeguard the future of the snow leopard in Pakistan and the region. These landscapes fall within mountain ranges of the Himalaya, Hindu Kush and Karakoram-Pamir, respectively. We also identified gaps in the existing protected areas network and suggest new protected areas in Chitral and Gilgit-Baltistan to protect critical habitats of snow leopard in Pakistan.