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[This corrects the article DOI: 10.1371/journal.pone.0297413.].
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The study draws attention to the associations between monetary and economic elements and their potential environmental impacts. The study uses time series data from 1960 to 2022 to examine the connection between CO2 emissions, industrial growth, GNE, and inflation in China. The researchers utilized the well-known econometric technique of nonlinear autoregressive distributed lag (NARDL) to examine nonlinear correlations between these variables. The results reveal that GDP, inflation, and economic development influence long-term CO2 emissions. The strong positive correlation between gross national expenditures and economic activity increases CO2 emissions. In the short run, CO2 emissions are positively and statistically significantly affected by inflation. While inflation temporarily affects CO2 emissions, this effect dissipates with time. Industrial activity increases CO2 emissions, and China's fast industrialization has damaged the environment. The energy-intensive fertiliser manufacturing process and fossil fuels increase CO2 emissions. The research shows how government officials and academics may collaborate to create tailored measures to alleviate the environmental impacts of economic activity.
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Dióxido de Carbono , Meio Ambiente , Desenvolvimento Industrial , Dióxido de Carbono/análise , China , Inflação , Desenvolvimento Econômico , Produto Interno BrutoRESUMO
Dengue is one of Pakistan's major health concerns. In this study, we aimed to advance our understanding of the levels of knowledge, attitudes, and practices (KAPs) in Pakistan's Dengue Fever (DF) hotspots. Initially, at-risk communities were systematically identified via a well-known spatial modeling technique, named, Kernel Density Estimation, which was later targeted for a household-based cross-sectional survey of KAPs. To collect data on sociodemographic and KAPs, random sampling was utilized (n = 385, 5 % margin of error). Later, the association of different demographics (characteristics), knowledge, and attitude factors-potentially related to poor preventive practices was assessed using bivariate (individual) and multivariable (model) logistic regression analyses. Most respondents (>90 %) identified fever as a sign of DF; headache (73.8 %), joint pain (64.4 %), muscular pain (50.9 %), pain behind the eyes (41.8 %), bleeding (34.3 %), and skin rash (36.1 %) were identified relatively less. Regression results showed significant associations of poor knowledge/attitude with poor preventive practices; dengue vector (odds ratio [OR] = 3.733, 95 % confidence interval [CI ] = 2.377-5.861; P < 0.001), DF symptoms (OR = 3.088, 95 % CI = 1.949-4.894; P < 0.001), dengue transmission (OR = 1.933, 95 % CI = 1.265-2.956; P = 0.002), and attitude (OR = 3.813, 95 % CI = 1.548-9.395; P = 0.004). Moreover, education level was stronger in bivariate analysis and the strongest independent factor of poor preventive practices in multivariable analysis (illiterate: adjusted OR = 6.833, 95 % CI = 2.979-15.672; P < 0.001) and primary education (adjusted OR = 4.046, 95 % CI = 1.997-8.199; P < 0.001). This situation highlights knowledge gaps within urban communities, particularly in understanding dengue transmission and signs/symptoms. The level of education in urban communities also plays a substantial role in dengue control, as observed in this study, where poor preventive practices were more prevalent among illiterate and less educated respondents.
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The study investigates the impacts of financial development, electricity use, and technology innovation on CO2 emissions. International trade also plays an essential role in the economic development of Pakistan. Studying the relationship between ecological parameters, technological innovation, electricity use, and CO2 emissions is necessary to formulate the country's reasonable and practical energy policies. Based on the study's use of data from 292 Pakistani firms from 2006 to 2021, the paper investigates the mechanism of the role between financial development, electricity use, technological innovation, international trade, and CO2 emissions using the spatial Durbin model. The results reveal that the effect of economic growth, electricity use, and technological innovation on CO2 emissions has a spatial spillover effect. The results verify that international trade and the transport sector promote the country's carbon emissions. The typical speculation spike on technology innovation enhanced and financial development should concentrate more on protective ecological parameters. The research provides theoretical guidance for solving the contradictory problem of renewable energy use growth and CO2 emission limitation while promoting green and low-carbon development in the country.
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A popular subject of general interest is the connection between corporate social responsibility, research and development, tax policy, and the purchase of green bonds. To encourage the coordinated growth of the economy and a pollution-free environment, it is crucial to understand how they interact. The paper develops a theoretical framework based on the general equilibrium theory's multi-sector model to examine how tax competition affects environmental degradation. The existence of such an effect, which is a threshold effect resulting from corporate social responsibility (CSR), and how it is impacted by CSR are theoretically established. The study used Moroccan province panel data from 2000 to 2022 and the spatial panel threshold model. The empirical finding demonstrates the importance of the threshold impact of CSR since reduced tax competition tends to worsen environmental degradation when CSR is above the threshold value and to reduce it when CSR is below the threshold value. The study also reveals that the impact of tax competitiveness varies regionally. Several policy suggestions are then put out to assist Morocco in reducing environmental pollution through taxation.
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Poluição Ambiental , Políticas , Responsabilidade Social , MarrocosRESUMO
The spatial-temporal assessment of vector diseases is imperative to design effective action plans and establish preventive strategies. Therefore, such assessments have potential public health planning-related implications. In this context, we here propose an integrated spatial disease evaluation (I-SpaDE) framework. The I-SpaDE integrates various techniques such as the Kernel Density Estimation, the Optimized Hot Spot Analysis, space-time assessment and prediction, and the Geographically Weighted Regression (GWR). It makes it possible to systematically assess the disease concentrations, patterns/trends, clustering, prediction dynamics, and spatially varying relationships between disease and different associated factors. To demonstrate the applicability and effectiveness of the I-SpaDE, we apply it in the second largest city of Pakistan, namely Lahore, using Dengue Fever (DF) during 2007-2016 as an example vector disease. The most significant clustering is evident during the years 2007-2008, 2010-2011, 2013, and 2016. Mostly, the clusters are found within the city's central functional area. The prediction analysis shows an inclination of DF distribution from less to more urbanized areas. The results from the GWR show that among various socio-ecological factors, the temperature is the most significantly associated with the DF followed by vegetation and built-up area. While the results are important to understand the DF situation in the study area and have useful implications for public health planning, the proposed framework is flexible, replicable, and robust to be utilized in other similar regions, particularly in developing countries in the tropics and sub-tropics.