Exploring the dynamic nexus between renewable energy, poverty alleviation, and environmental pollution: fresh evidence from E-9 countries.

The present study aims to scrutinize the long- and short-run relationship along with the direction of causality among environmental pollution (CO2), renewable, non-renewable energy, income disparity, exchange rate, and poverty alleviation in E-9 countries of continent Asia, using a panel dataset from 1990 to 2018. The current study used pooled mean group autoregressive distributed lag (PMG ARDL) and Dumitrescu-Hurlin (D-H) causality test after affirming a stable long-run association among environmental pollution and all the explanatory variables. However, ECM (error correction mechanism) was specified to explore short-run dynamics. The study's outcomes confirmed strong co-integration among environmental pollution (CO2), renewable, non-renewable energy, income disparity, exchange rate, and poverty alleviation. Moreover, uni (bi) directional causality runs from non-renewable energy, exchange rate, and income disparity (poverty alleviation and renewable energy) to environmental pollution (CO2). Results also revealed that poverty alleviation, exchange rate, and renewable energy usage substantially negatively influence environmental pollution (CO2). Contrarily, income disparities and non-renewable energy usage positively influence long- and short-run environmental pollution. Therefore, from the policy perspective, the current study focused on twofold; first, there is a desire to alleviate poverty, the decline in non-renewable energy use and income disparity among upper and lower-income quintiles. Second, boost exchange rate and renewable energy use to control environmental pollution in the described least developed countries (LDCs).

Evaluating the efficiency of coarser to finer resolution multispectral satellites in mapping paddy rice fields using GEE implementation.

Timely and accurate estimation of rice-growing areas and forecasting of production can provide crucial information for governments, planners, and decision-makers in formulating policies. While there exists studies focusing on paddy rice mapping, only few have compared multi-scale datasets performance in rice classification. Furthermore, rice mapping of large geographical areas with sufficient accuracy for planning purposes has been a challenge in Pakistan, but recent advancements in Google Earth Engine make it possible to analyze spatial and temporal variations within these areas. The study was carried out over southern Punjab (Pakistan)-a region with 380,400 hectares devoted to rice production in year 2020. Previous studies support the individual capabilities of Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) for paddy rice classification. However, to our knowledge, no study has compared the efficiencies of these three datasets in rice crop classification. Thus, this study primarily focuses on comparing these satellites' data by estimating their potential in rice crop classification using accuracy assessment methods and area estimation. The overall accuracies were found to be 96% for Sentinel-2, 91.7% for Landsat-8, and 82.6% for MODIS. The F1-Scores for derived rice class were 83.8%, 75.5%, and 65.5% for Sentinel-2, Landsat-8, and MODIS, respectively. The rice estimated area corresponded relatively well with the crop statistics report provided by the Department of Agriculture, Punjab, with a mean percentage difference of less than 20% for Sentinel-2 and MODIS and 33% for Landsat-8. The outcomes of this study highlight three points; (a) Rice mapping accuracy improves with increase in spatial resolution, (b) Sentinel-2 efficiently differentiated individual farm level paddy fields while Landsat-8 was not able to do so, and lastly (c) Increase in rice cultivated area was observed using satellite images compared to the government provided statistics.

Reflections of Pro-Poor Growth across Agro-Climatic Zones for Farming and Non-Farming Communities: Evidence from Punjab, Pakistan.

The basic objective of the existing study was to inspect the triangular association between economic growth, poverty, and income disparity in farming and non-farming communities across agro-climatic zones in Punjab province, Pakistan. The cross-sectional Household Integrated Economic Survey (HIES) data and Poverty Equivalent Growth Rate (PEGR) methodology were applied from 2001-2002 to 2015-2016. Outcomes of the study found that in a short period, 2001-2002 to 2004-2005; 2004-2005 to 2005-2006; 2005-2006 to 2007-2008; 2007-2008 to 2010-2011; 2010-2011 to 2011-2012; 2011-2012 to 2013-2014; and 2013-2014 to 2015-2016, economic growth has presented hybrid (pro-poor and anti-poor) pattern across both communities of all agro-climatic zones of Punjab province in different periods. In the longer period of 2001-2002 to 2015-2016, economic growth has been pro-poor across both communities of all the zones apart from zone III (Cotton-Wheat Punjab); there is an anti-poor pattern of economic growth. Results for the decomposition of change in poverty further indicate that economic growth is a dominant factor in reducing poverty for all investigated zone. Moreover, a positive redistribution component reduces the beneficial impacts of economic growth for the poor more than for non-poor, that ultimately makes economic development patterns anti-poor in zone III. In the present study, we proposed two-fold policy implications. First, improve the living standard of households in each agro-climatic zone by increasing their incomes. Second, develop a precise taxation system that helps to reduce income disparities among upper-pro to lower-income groups.

Modeling the impact of climatic and non-climatic factors on cereal production: evidence from Indian agricultural sector.

The underpinned study examines the effects of climatic and non-climatic factors on Indian agriculture, cereal production, and yield using the country-level time series data of 1965-2015. With the autoregressive distributed lag (ARDL) bounds testing approach, the long-term equilibrium association among the variables has been explored. The results reveal that climatic factors like CO2 emissions and temperature adversely affect agricultural output, while rainfall positively affects it. Likewise, non-climatic factors, including energy used, financial development, and labor force, affect agricultural production positively in the long run. The estimated long-run results further demonstrate that CO2 emissions and rainfall positively affect both cereal production and yield, while temperature adversely affects them. The results exhibit that the cereal cropped area, energy used, financial development, and labor force significantly and positively impact the long-run cereal production and yield. Finally, pairwise Granger causality test confirmed that both climatic and non-climatic factors are significantly influencing agriculture and cereal production in India. Based on these results, policymakers and governmental institutions should formulate coherent adaptation measures and mitigation policies to tackle the adverse climate change effects on agriculture and its production of cereals.

Exploring the Relationships Between a Toxic Workplace Environment, Workplace Stress, and Project Success with the Moderating Effect of Organizational Support: Empirical Evidence from Pakistan.

PURPOSE: Researchers have shown great interest in the relationships among a toxic workplace environment, workplace stress, and project success, which have led to an expansive body of research on the topic. In light of this work, the current study explores the effects of a toxic workplace environment (TWE) and workplace stress (WS) as determinants of project success in the renewable energy projects of Pakistan. Based on the resource-based view (RBV) theory, the study proposes and tests a model with organizational support as a moderating variable. RESEARCH METHODOLOGY: A 30-item questionnaire survey was administered among staff of ten renewable energy project companies located in the vicinity of Karachi, Lahore, Islamabad (Pakistan). The target population was senior managers, middle-level managers, and administrative staff. Structural equation modelling was used to estimate the predictive power of the model. RESULTS: A toxic workplace environment was found to have negative relationships with project success and workplace stress. Organizational support served as a moderator in the relationship between a toxic workplace environment and workplace stress and thus contributed to the success of a project. CONCLUSION: Toxic workplace environment and the resulting workplace stress have a negative effect on project success. Projects undertaken in the energy sector have tight deadlines, which create stress that leads to a range of mental and physical health problems. Workers facing these problems can ultimately suffer from such diseases as depression, anxiety, and insomnia. These issues lower morale and, thus, negatively affect productivity. The provision of organizational support can mitigate the negative effects.

How does energy poverty affect economic development? A panel data analysis of South Asian countries.

This study examines the interaction between energy poverty, employment, education, per capita income, inflation, and economic development using panel data for seven South Asian countries. The present study uses panel data spanning the period from 1995 to 2017, panel cointegration, autoregressive distributed lag (ARDL), and penalized quantile regression (PQR) estimators to test for cointegration in the long-run. The estimated results reveal that both panel cointegration approaches (Pedroni and Johansen-Fisher) demonstrate the existence of the long-term relationship between energy poverty, employment, education, per capita income, inflation, and economic development. The ARDL estimates show that energy poverty has a negative influence on economic development in both the long-run and the short-run. The results provide support for economic, social, and environmental policymakers in their decision-making. This study suggests that, in relation to financing the green and low-carbon economy concept, both the public sector and private industries need to make further efforts to use modern, energy-efficient, and green technologies, which are beneficial both for economic progress as well as managing the ecological degradation process.

The Role of Agriculture and Foreign Remittances in Mitigating Rural Poverty: Empirical Evidence from Pakistan.

BACKGROUND: This study examines the role of the agriculture and foreign remittances in mitigating rural poverty in Pakistan. METHODS: The data used relate to the period 1980-2017 and are sourced from the World Bank and the Economic Survey of Pakistan produced annually by the Ministry of Finance. The ARDL technique was used to calculate the effects of agriculture and foreign remittances on rural poverty. RESULTS: The results of this study indicate that agriculture helps to mitigate rural poverty in the long run, but that foreign remittances are more effective in reducing rural poverty in the short run. In this paper, results confirm the existence of correlations between agriculture, foreign remittances and rural poverty. CONCLUSION: The outcomes of this study support the call for the government to introduce agricultural credit schemes for the rural population of Pakistan. Moreover, the government should take steps to enhance diplomatic relations with other countries and simplify policies and visa application procedures for Pakistani workers. Finally, this study suggests the government should simplify procedures for the transfer of foreign remittances to Pakistan.

Using GIS tools to detect the land use/land cover changes during forty years in Lodhran District of Pakistan.

Land use/land cover (LULC) change has serious implications for environment as LULC is directly related to land degradation over a period of time and results in many changes in the environment. Monitoring the locations and distributions of LULC changes is important for establishing links between regulatory actions, policy decisions, and subsequent LULC activities. The normalized difference vegetation index (NDVI) has the potential ability to identify the vegetation features of various eco-regions and provides valuable information as a remote sensing tool in studying vegetation phenology cycles. Similarly, the normalized difference built-up index (NDBI) may be used for quoting built-up land. This study aims to detect the pattern of LULC, NDBI, and NDVI change in Lodhran district, Pakistan, from the Landsat images taken over 40 years, considering four major LULC types as follows: water bodies, built-up area, bare soil, and vegetation. Supervised classification was applied to detect LULC changes observed over Lodhran district as it explains the maximum likelihood algorithm in software ERDAS imagine 15. Most farmers (46.6%) perceived that there have been extreme changes of onset of temperature, planting season, and less precipitation amount in Lodhran district in the last few years. In 2017, building areas increased (4.3%) as compared to 1977. NDVI values for Lodhran district were highest in 1977 (up to + 0.86) and lowest in 1997 (up to - 0.33). Overall accuracy for classification was 86% for 1977, 85% for 1987, 86% for 1997, 88% for 2007, and 95% for 2017. LULC change with soil types, temperature, and NDVI, NDBI, and slope classes was common in the study area, and the conversions of bare soil into vegetation area and built-up area were major changes in the past 40 years in Lodhran district. Lodhran district faces rising temperatures, less irrigation water, and low rainfall. Farmers are aware of these climatic changes and are adapting strategies to cope with the effects but require support from government.

Study of land cover/land use changes using RS and GIS: a case study of Multan district, Pakistan.

Water and land both are limited resources. Current management strategies are facing multiple challenges to meet food security of an increasing population in numerous South Asian countries, including Pakistan. The study of land cover/land use changes (LCLUC) and land surface temperature (LST) is important as both provide critical information for policymaking of natural resources. We spatially examined LCLU and LST changes in district Multan, Pakistan, and its impacts on vegetation cover and water during 1988 to 2017. The LCLUC indicate that rice and sugarcane had less volatility of change in comparison with both cotton and wheat. Producer's accuracy (PA) is the map accuracy (the producer of map), but user's accuracy (UA) is the accuracy from the point of view of a map user, not the map maker. Average overall producer's and user's accuracy for the region was 85.7% and 87.7% for Rabi (winter) and Kharif (summer) seasons, respectively. The results of this study showed that 'built-up area' increased with 7.2% of all the classes during 1988 to 2017 in the Multan district. Anthropogenic activities decreased the vegetation, leading to an increase in LST in study area. Changes on LCLU and LST during the last 30 years have shown that vegetation pattern has changed and temperature has increased in the Multan district.

Radiation efficiency and nitrogen fertilizer impacts on sunflower crop in contrasting environments of Punjab, Pakistan.

Sunflower (Helianthus annuus L.) is the leading non-conventional oilseed crop in Pakistan. Nitrogen fertilizer can affect plant growth and productivity by changing canopy size which has an effect on the radiation use efficiency (RUE) of the crop. The response of sunflower hybrids in terms of phenology, fraction of intercepted radiation (F i), and RUE to nitrogenous rates (0, 60, 120, 180, and 240 kg ha-1) was studied in three field experiments conducted in three various environments: Multan (arid), Faisalabad (semi-arid), and Gujranwala (sub-humid) during spring seasons 2008 and 2009. The treatments were laid out according to a randomized complete block design with split plot arrangements, keeping the sunflower hybrids in main plots and nitrogen rates in sub-plots, and replicated three times. The results showed Hysun-38 took a maximum number of days to anthesis (101) as compared to Pioneer-64A93 (100) and Hysun-33 (99). The mean values of F i were 0.850, 0.903, and 0.978, and the estimated values of RUE for total aboveground dry matter were 2.14, 2.47, and 2.65 g MJ-1 at experimental locations of Multan, Faisalabad, and Gujranwala, respectively. The values of RUE for grain yield (RUEGY) were 0.78, 0.98, and 1.26 g MJ-1 at experimental locations of Multan, Faisalabad, and Gujranwala, respectively. The average RUEGY values over three locations were 2.61, 2.60, 2.43, and 2.36 g MJ-2 in N4 (180 kg ha-1), N5 (240 kg ha-1), N3 (120 kg ha-1), and N2 (60 kg ha-1) treatments, respectively. Increasing rates of N increased RUEGY over the standard treatment N3 (120 kg N ha-1); however, the averaged values over three locations were 1.22, 1.08, 0.99, and 0.92 g MJ-2 in N4, N5, N3, and N2 treatments, respectively. Therefore, optimum water and N doses are important for attaining higher RUE, which may enhance sunflower growth and yield.

Optimizing the phosphorus use in cotton by using CSM-CROPGRO-cotton model for semi-arid climate of Vehari-Punjab, Pakistan.

Crop nutrient management is an essential component of any cropping system. With increasing concerns over environmental protection, improvement in fertilizer use efficiencies has become a prime goal in global agriculture system. Phosphorus (P) is one of the most important nutrients, and strategies are required to optimize its use in important arable crops like cotton (Gossypium hirsutum L.) that has great significance. Sustainable P use in crop production could significantly avoid environmental hazards resulting from over-P fertilization. Crop growth modeling has emerged as an effective tool to assess and predict the optimal nutrient requirements for different crops. In present study, Decision Support System for Agro-technology Transfer (DSSAT) sub-model CSM-CROPGRO-Cotton-P was evaluated to estimate the observed and simulated P use in two cotton cultivars grown at three P application rates under the semi-arid climate of southern Punjab, Pakistan. The results revealed that both the cultivars performed best at medium rate of P application (57 kg ha-1) in terms of days to anthesis, days to maturity, seed cotton yield, total dry matter production, and harvest index during 2013 and 2014. Cultivar FH-142 performed better than MNH-886 in terms of different yield components. There was a good agreement between observed and simulated days to anthesis (0 to 1 day), days to maturity (0 to 2 days), seed cotton yield, total dry matter, and harvest index with an error of -4.4 to 15%, 12-7.5%, and 13-9.5% in MNH-886 and for FH-142, 4-16%, 19-11%, and 16-8.3% for growing years 2013 and 2014, respectively. CROPGRO-Cotton-P would be a useful tool to forecast cotton yield under different levels of P in cotton production system of the semi-arid climate of Southern Punjab.