Hafiz Muhammad Naveed

The load capacity factor (LCF) allows for a thorough investigation of environmental deterioration using the ecological footprint and biocapacity comparison to track a certain ecological threshold. It is evident that when the LCF decreases, environmental deterioration increases. Until now, researchers have traditionally examined environmental concerns through empirical methods, utilizing indicators like carbon dioxide emissions, nitrogen oxide emissions, sulfur dioxide emissions, ecological footprint, and others. Environmental supply‐side challenges may be overlooked due to the use of these indicators. By recommending the LCF and applying it to examine the variables influencing environmental health, this study fills a research gap in the field of environmental health. Monitoring environmental health is made easier by the load capacity factor, which shows the difference between biocapacity and ecological footprint. This study intends to analyze the impact of green technological innovation (TI), financial development (FND), population aging (PAG), energy consumption (ENC), and economic growth (GDP) on the LCF, which emphasizes environmental challenges from the perspectives of both supply and demand sides, to make up for this deficiency. However, the present study employs a Bayesian neural network (BNN) on 15 Regional Comprehensive Economic Cooperation (RCEP) countries datasets from 1980–2020 to estimate the interaction between TI, FND, and LCF. Additionally, in order to guarantee the stability of our findings, we applied the generalized method of moments (GMM). The BNN results show that when FND, ENC, and GDP increase, the LCF decreases; however, TI and PAG increase LCF and environmental sustainability. According to the study, the governments of the RCEP countries should encourage investment and initiatives in green technology innovation as a way to improve ecological sustainability. The article offers valuable policy recommendations for ensuring a fair and sustainable level of environmental quality in RCEP countries.

Background: Why do politically salient food policies persist even when they may generate long-term developmental costs? This paper develops and empirically examines the concept of nutritional lock-in, a framework in which food-policy reforms can alter early-life nutritional conditions and, in turn, shape subsequent human-capital outcomes and dimensions of civic capacity. Method: We study Pakistan's 2008–2010 wheat-subsidy reforms, exploiting district-level variation in pre-reform exposure to the public distribution system in a difference-in-differences framework. Additionally, study also used three waves of the Pakistan Demographic and Health Survey (2006–07, 2012–13, and 2017–18) to find the impact on children heights-for-age. Results: Study find that children in districts with greater pre-reform exposure experienced a decline of approximately 0.28 standard deviations in height-for-age following the reforms. The estimated effect remains similar after controlling for child, maternal, and household characteristics and in sibling fixed-effects specifications. Complementary analyses indicate substantial changes in dietary composition, including lower dietary diversity and reduced consumption of several nutrient-rich food groups. District-cohort analyses further show that early-life nutritional shocks are negatively associated with later standardized mathematics achievement, although these estimates are correlational. Similarly, women in more highly exposed districts exhibit lower scores on measures of autonomy, mobility, media exposure, and community participation, which we interpret as proxies for civic capacity rather than direct measures of political behavior. Conclusion: The study findings are consistent with the nutritional lock-in framework: food-policy reforms can have consequences extending beyond immediate fiscal or consumption effects to child nutrition, human-capital formation, and dimensions of civic capacity. However, the data do not identify the complete causal pathway from childhood nutrition to political accountability or policy persistence. The findings therefore motivate a broader view of food policy as a component of long-run human-capital and institutional development.

Background: Child undernutrition remains a major public health concern and creates health-related inequalities in developing countries, with long-term consequences for human development deficits. Most of these early studies were conducted using conventional econometric techniques; however, understanding the complex, multi-level determinants of undernutrition may require more advanced analytical methods. Methods: This study, PC algorithm is used for Structural Learning-Based Bayesian Networks [SL-BN] (a graphical model based on directed acyclic graph with joint and conditional associations)—which is conceptually different than the weight-based networks, to examine the macro- and micro-level determinants of child undernutrition in Pakistan. Macro-level analysis combines historical nutrition policy evaluation (critical discourse) with WDI and UNDP data (2000–2022), spanning the MDG and SDG eras. Micro-level analysis draws on the Pakistan Demographic and Health Survey (PDHS 2017–18) to identify socioeconomic drivers of child malnutrition and nutritional inequalities. Results: In micro analysis, the SL-BN network structure identifies household-level determinants—such as poor wealth status, rural residence, poor environmental conditions, low maternal education, and diarrhea—as key drivers of child undernutrition. Inequality is stark, with malnutrition disproportionately concentrated among the poorest. However, the SL-BN findings and critical assessment of nutritional policies and programs found that low investment in human development, foreign dependency on health, poverty, inflation, and a history of inconsistent nutritional policies to combat hunger and undernutrition are the fundamental issues at the macro level in Pakistan. Conclusion: The study concluded that the underlying micro level determinants of child undernutrition in Pakistan cannot overcome without long-term targeted policies at macro level.

Climate change requires an integrated policy framework that can handle multifaceted interrelations between energy systems, financial systems, technological innovation, and demographic processes. This research explores implicit drivers of CO ₂ intensity and ecological footprint in 13 developed and developing countries from 2001 to 2025 using an interpretable MNN–DeepSHAP framework. The results indicate that better and more consistent environmental impacts are generated through improved efficiency of renewable energy, especially through lower levelized costs and better production performance rather than simply increasing installed capacity. Green technological innovation and structural energy transition can help to lower emissions, particularly when green investment is supported by well‐developed financial markets. But economic expansion alone does not ensure ecological sustainability, whereas population growth creates persistent upward pressure on environmental demand. This reflects a mitigation–expansion trap in which scale effects offset technological progress. The study emphasizes the importance of integrated policy packages.

Accurate identification of modifiable risk factors for child malnutrition is of great importance in the formulation of policies to protect children's health in South Asia. Current research has developed an innovative analytical system that uses deep learning algorithms to accurately assess the nutritional status of children and its relevance to indoor parental smoking pollution, at threshold level in South Asia. Data on 219,168 under-five children were analyzed from recent (2016-2022) nationally representative Demographic and Health Survey (DHS) for five South Asian countries: Bangladesh, India, Maldives, Nepal, and Pakistan. Our method first applied a comprehensive pre-processing and feature engineering pipeline to detect key risk patterns. By conducting a thorough benchmark of 16 deep learning models, the Bayesian Neural Network (BNN) was identified as the optimal model for predictive inference and risk quantification. The BNN analysis, supported by Mesh query graphs, showed strong co-association of parental exposure (tobacco smoking) and child undernutrition. Moreover, the association was dose-dependent in that predicted risk for child malnutrition increased substantially when the frequency of cigarette smoking exceeded ten cigarettes per day. This study importantly found that the marginal risk of malnutrition increases by 3.2 times with additional consumption of cigarette after threshold. There is a strong joint association between child malnutrition and parental smoking. Additionally, less than equal to 10 cigarettes considered the threshold smoking level, greater than 10 cigarettes per day higher the risk of malnutrition by 3.2% with additional cigarette consumption. This evidence provides considerable leverage for policymakers as our results suggest that modern AI methods can effectively inform interventions targeted at increasing the prevalence of smoke-free homes and improving child nutrition in South Asia. Not applicable. This study is a secondary analysis of publicly available, de-identified data from the Demographic and Health Survey, and does not report the results of a prospective health care intervention.

Child undernutrition remains one of the most entrenched and inequitable global health problems that significantly impacts human capital, economic development, and intergenerational equity [1–4]. Almost two in five children under five years of age suffer from stunting, which is considered a marker of chronic malnutrition associated with increased mortality, impaired cognitive development, and decreased earning […]

Malnutrition remains an urgent global health issue, especially in developing countries, where it accounts for half of all deaths among children under the age of five [1,2]. The World Health Organization (WHO) recommends intake from at least five of eight food groups as a minimum threshold for nutritional adequacy, while emphasising the crucial role of […]