The pressing global effort to tackle CO2 emissions has brought about a strong emphasis on adopting green technology by economies striving for low-carbon development. Within this context, this research investigates the environmental significance of green technology and exports in Malaysia. By examining 30-year data from 1989 to 2019 and utilising the autoregressive distributed lag model (ARDL), this study explores these variables' long-run and short-run effects on Malaysia's environment. The outcomes reveal noteworthy insights: population growth and green technology negatively impact environmental degradation, whereas exports and economic expansion contribute to environmental depletion over the long term. However, the influences of a higher population and exports are inconsequential in the short term. Additionally, the study captures the influences of transient economic challenges, such as the COVID-19 outbreak. Consequently, the study emphasises crucial policy implications for the Malaysian government. Firstly, it strongly recommends increasing investment in sustainable technology, especially within the manufacturing sector, to mitigate the adverse environmental impact of exports. Furthermore, it suggests incentivizing companies to embrace green technology through subsidies for acquiring renewable energy and imposing higher taxes on non-renewable energy sources. Additionally, policymakers are urged to prioritise human capital development by raising public awareness about the dangers of heightened CO2 emissions. Malaysia can leverage its expertise to foster economic expansion without compromising the environment by engaging the working population in environmentally sustainable economic activities. These policy recommendations aim to expedite the shift towards a decarbonised economy, promote sustainable development, and safeguard Malaysia's natural resources.
The present study aimed to determine the factors associated with CKD stage 3b among type 2 diabetics attending primary care follow-up, specifically the role of angiotensin blockade dosage. This was a pilot unmatched case-control study conducted in a teaching primary care centre. Clinical data of 25 cases of diabetic patients with CKD stage 3b (GFR 30-45ml/min/1.73m2) in 2012 were selected for this study, as well as 103 controls who were diabetic patients with GFR more than 45ml/min/1.73m2 in 2012. Systematic random sampling was employed. Data was obtained from patients’ diabetic records, computerised clinical medical information system and medical case notes. Univariate analysis was done using Chi-square, t-test, Fisher’s exact test and Mann-Whitney U-test. Multiple logistic regression was used to determine the associated factors for development of CKD stage 3b. Cases and controls were different in terms of age, duration of diabetes, use and dosage of angiotensin blockade medications, systolic blood pressure and baseline GFR. Multiple logistic regression revealed that systolic blood pressure (Adjusted OR= 1.08, 95% CI= 1.02-1.14, p=0.013) and baseline GFR (Adjusted OR= 0.90, 95% CI= 0.85-0.95, p
In the wake of various catastrophic consequences of climate change, Malaysia, a rapidly developing economy, is also inevitably experiencing environmental degradation that merits prompt and serious attention from policymakers and its government. Hence, this study simultaneously highlights the short and long-run dynamic connections between carbon emission in Malaysia and the trio of corruption levels, foreign investment inflow, and trade liberalization. The study also controls for a combination of other factors including energy use, GDP, and urbanization. A robust empirical analysis was conducted on time series observations for the country based on the recent Dynamic ARDL simulation. It was observed that Malaysia's per capita pollution levels significantly reduces based on the corruption perception levels during the sampling period while the economic expansion's effect on emission levels is positive. Additionally, urbanization, trade levels and energy use all aggravate the emission levels. On the other hand, although FDI poses an insignificant environmental damage in the short run, its environmental sustainability enhancement roles were supported by its long-run negative impacts on carbon emission. Lastly, the EKC was established and as such, essential policy directions were provided for stakeholders in the rapidly emerging Malaysian economy.
Poverty, an intricate global challenge influenced by economic, political, and social elements, is characterized by a deficiency in crucial resources, necessitating collective efforts towards its mitigation as embodied in the United Nations' Sustainable Development Goals. The Gini coefficient is a statistical instrument used by nations to measure income inequality, economic status, and social disparity, as escalated income inequality often parallels high poverty rates. Despite its standard annual computation, impeded by logistical hurdles and the gradual transformation of income inequality, we suggest that short-term forecasting of the Gini coefficient could offer instantaneous comprehension of shifts in income inequality during swift transitions, such as variances due to seasonal employment patterns in the expanding gig economy. System Identification (SI), a methodology utilized in domains like engineering and mathematical modeling to construct or refine dynamic system models from captured data, relies significantly on the Nonlinear Auto-Regressive (NAR) model due to its reliability and capability of integrating nonlinear functions, complemented by contemporary machine learning strategies and computational algorithms to approximate complex system dynamics to address these limitations. In this study, we introduce a NAR Multi-Layer Perceptron (MLP) approach for brief term estimation of the Gini coefficient. Several parameters were tested to discover the optimal model for Malaysia's Gini coefficient within 1987-2015, namely the output lag space, hidden units, and initial random seeds. The One-Step-Ahead (OSA), residual correlation, and residual histograms were used to test the validity of the model. The results demonstrate the model's efficacy over a 28-year period with superior model fit (MSE: 1.14 × 10-7) and uncorrelated residuals, thereby substantiating the model's validity and usefulness for predicting short-term variations in much smaller time steps compared to traditional manual approaches.