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This study evaluates the performance of green finance and investment scenarios in 15 carbon emitting countries, among which 7 are developed countries and 8 are developing countries. The principal component analysis is applied to form the global green financing (GF) and investment index, a composite indicator for assessing the multidimensional characteristics of GF and investment. The global green finance and investment index is developed to map the country’s overall GF and investing scenario. The indicator is developed on the basis of 30 variables that represent 11 quantitative factors. These factors are aggregated into four parameters: transparency, efficiency, efficacy and resilience. Transparency includes political stability and the development of the countries’ capital markets to adapt to the green transition. Efficiency consists of the performance of existing resources and regulatory conditions of the countries. Efficacy refers to the factors related to international engagement and the growth of specific financial instruments. Lastly, resilience includes factors that promote the adaptability of the countries towards a green economy and green financial system. It contains the regulatory structure of the country’s growth of macroeconomic variables. These variables represent social, economic, environmental and governance factors that influence the countries’ GF and investment scenario. The countries are ranked on the basis of the composite indicator score. The USA scored the highest rank, and India scored the least. In terms of developed countries, the USA has achieved the highest value, followed by Germany and in developing countries, China has scored the highest performance, followed by Mexico.

Accurate solar photovoltaic models (SPVM) are critical for optimizing solar photovoltaic (PV) capacity to convert sunlight into electricity. The simulation and design of PV systems rely on estimating unknown constraints from solar photovoltaic (SPV) cells. Each parameter plays a crucial task in the output properties of an SPV under actual environmental conditions. Optimizing the unknown constraints of the SPVM is not an easy task due to the nonlinear characteristics of the PV cell. This study aims to develop a novel metaheuristic algorithm, enhanced dynamic inertia particle swarm optimization (EDIPSO) algorithm with velocity clamping, to establish all the seven and five constraints of the two-diode model (TDM) and one-diode model (ODM).

In complex parameter spaces, the conventional particle swarm optimization (PSO) approach typically leads to poor convergence because it fails to balance exploration and exploitation. The proposed approach is an EDIPSO with velocity clamping to minimize the possibility of overshooting possible solutions and improve stability. Velocity clamping is also used to prevent particle velocities from rising over specified limitations. Beginning the process with a large inertia weight to promote exploration and progressively decreasing it to improve exploitation, leading to a thorough analysis of the search space. The algorithm is implemented to investigate the accuracy of estimated constraint values of RTC-France (RTC-F) solar cell, Photo watt-PWP 201 SPV module (PWP 201 SPV), KC 200GT SPV module (KC 200 GT SPV) for ODM and TDM.

The proposed approach is used to extract the seven and five constraints of the TDM and ODM under standard test conditions for three different SPV modules. Thorough simulation and statistical analysis indicate that the EDIPSO with velocity clamping may outperform other cutting-edge optimization algorithms exclusively regarding accuracy, computational time and reliability.

An enhanced dynamic inertia PSO is suggested for determining the parameters of the TDM and ODM in SPV modules. This method specifically accounts for the recombination saturation current within the p–n junction’s depletion region, without overlooking or assuming away any parameters, thereby achieving greater accuracy. When comparing the estimated constraints of TDM and ODM for various SPVs, EDIPSO almost precisely aligns the data from the proposed model with the practical data. Thus, the proposed method for calculating the SPV model parameter may exhibit to be a feasible and efficient solution.

This study aims to delve into the impact of the Pradhan Mantri Jan Arogya Yojana (PMJAY) on the well-being and quality of life of beneficiaries in Surat City of India. Employing correlation and regression analyses, the study uncovers significant correlations between Awareness, Healthcare Utilisation, and Financial Burden Reduction with well-being outcomes.

The investigation employs a structured questionnaire to gather data from 250 beneficiaries, exploring the relationships between Awareness, Healthcare Utilisation, Financial Burden Reduction, Well-Being and quality of Life. The data was collected using a structured questionnaire using a survey method.

The results highlighted the crucial role of Awareness in empowering beneficiaries to make informed healthcare decisions, positively influencing their well-being. Furthermore, the study underscores how active engagement with PMJAY's healthcare services enhances well-being. The mitigation of financial burdens emerges as a pivotal factor, signifying the program's efficacy in improving beneficiaries' quality of life.

The comprehensive model presented in this study reveals that PMJAY's multifaceted approach is pivotal in promoting enhanced well-being and quality of life among beneficiaries. These findings affect public health policies seeking to create holistic interventions that holistically address vulnerable populations' healthcare access, financial burdens, and overall well-being.