This precise study performed a focalized investigation to examine the association of environmental effects, new product development performance, superior customers’ value, and corporate social responsibility (CSR) on sustainable performance. This research study aimed to investigate how social media marketing application moderates the association between corporate social responsibility and sustainable performance of the firms located in Multan Division, Pakistan. This study applied a simple random sampling approach to execute this research, and the authors sent a questionnaire with an invitation letter and informed consent form to 752 respondents. Based on 548 valid responses from the targeted population, the first step was to screen and analyze data through Statistical Package for the Social Sciences (SPSS-V25) and the Smart PLS V-3.2.8. The results indicated that corporate social responsibility presented a positive impact on firms’ sustainable performance. The findings also revealed that social media marketing tools moderated the relationship between CSR and sustainable production of business firms. As a final point, the study only included respondents from Multan Division, therefore, limiting the generalizability of the findings to other Pakistani business firms. The implications of this study may provide further directions for researchers and academicians to consider the larger sample size and the addition of new variables in other regions worldwide. The findings are useful for filling the gap between the relationship of environmental effects, CSR, and social media marketing application to calculate the sustainable performance of business firms.

Restrictions on human and industrial activities due to the coronavirus (COVID-19) pandemic have resulted in an unprecedented reduction in energy consumption and air pollution around the world. Quantifying these changes in environmental conditions due to government-enforced containment measures provides a unique opportunity to understand the patterns, origins and impacts of air pollutants. During the lockdown in Pakistan, a significant reduction in energy demands and a decline of ∼1786 GWh (gigawatt hours) in electricity generation is reported. We used satellite observational data for nitrogen dioxide (NO₂), carbon monoxide (CO), sulphur dioxide (SO₂), aerosol optical depth (AOD) and land surface temperature (LST) to explore the associated environmental impacts of shifts in energy demands and emissions across Pakistan. During the strict lockdown period (March 23 to April 15, 2020), we observed a reduction in NO₂ emissions by 40% from coal-based power plants followed by 30% in major urban areas compared to the same period in 2019. Also, around 25% decrease in AOD (at 550 nm) thickness in industrial and energy sectors was observed although no major decrease was evident in urban areas. Most of the industrial regions resumed emissions during the 3rd quarter of April 2020 while the urban regions maintained reduced emissions for a longer period. Nonetheless, a gradual increase has been observed since April 16 due to relaxations in lockdown implementations. Restrictions on transportation in the cities resulted in an evident drop in the surface urban heat island (SUHI) effect, particularly in megacities. The changes reported as well as the analytical framework provides a baseline benchmark to assess the sectoral pollution contributions to air quality, especially in the scarcity of ground-based monitoring systems across the country.

Spatial–temporal rainfall assessments are integral to climate/hydrological modeling, agricultural studies, and water resource planning and management. Herein, we evaluate spatial–temporal rainfall trends and patterns in Pakistan for 1961–2020 using nationwide data from 82 rainfall stations. To assess optimal spatial distribution and rainfall characterization, twenty-seven interpolation techniques from geo-statistical and deterministic categories were systematically compared, revealing that the empirical Bayesian kriging regression prediction (EBKRP) technique was best. Hence, EBKRP was used to produce and utilize the first normal annual rainfall map of Pakistan for evaluating spatial rainfall patterns. While the largest under-prediction was estimated for Hunza (− 51%), the highest and lowest rainfalls were estimated for Malam Jaba in Khyber Pakhtunkhwa province (~ 1700 mm), and Nok-kundi in Balochistan province (~ 50 mm), respectively. A gradual south-to-north increase in rainfall was spatially evident with an areal average of 455 mm, while long-term temporal rainfall evaluation showed a statistically significant (p = 0.05) downward trend for Sindh province. Additionally, downward inter-decadal regime shifts were detected for the Punjab and Sindh provinces (90% confidence). These results are expected to help inform environmental planning in Pakistan; moreover, the rainfall data produced using the optimal method has further implications in several aforementioned fields.