The restoration of the ancient Silk Road intends to reconnect China with Africa, the Middle East, and Europe through a railway network, airports, roads, seaports, and an optical fiber system. The Belt and Road Initiative (BRI) has three components. One Belt, One Road (OBOR) is based upon two parts of the BRI; the maritime Silk Road and the Silk Road economic belt. OBOR is based upon six economic corridors. The China-Pakistan Economic Corridor (CPEC) is the smartest corridor under OBOR, which passes only through Pakistan, and after completion, will provide a safe and cheap route for China to import oil and energy. CPEC is a multidimensional project under which much infrastructure development initiative has been started to improve the infrastructure and economic development of Pakistan. Infrastructure development is an essential requirement in economic growth, one which further leads to industrialization and is helpful in economic development. The present study was conducted in Pakistan and explored how infrastructure development under the CPEC is useful for the sustainable development of Pakistan, as well as which kind of infrastructure development projects have been included in the CPEC to improve the socio-economic paradigm of Pakistan. A sample of 500 respondents was selected through a multistage sampling technique from the two-node cities. A questionnaire survey was used to collect primary data. The results of the study show that the CPEC is a catalyst for Pakistan to improve its socio-economic conditions and to achieve sustainable development. The participants of the survey agreed that CPEC will improve the socio-economic paradigm of Pakistan and will be helpful in the achievement of sustainable development goals.

Achieving the Sustainable Development Goals (SDGs) remains a significant challenge for many countries, particularly in the face of increasing environmental pollution. Balancing social, economic, and environmental sustainability under these conditions is especially complex. This study explores the role of green finance in promoting sustainable infrastructure, innovation in green technology, corporate social responsibility, economic stability, and environmental conservation within the framework of Belt and Road initiative (BRI), with a specific focus on the China-Pakistan Economic Corridor (CPEC) initiatives. Furthermore, the study examines the role of government support in facilitating the issuance of GF, emphasizing its significance in large-scale international development projects like CPEC. Data were collected through a structured questionnaire targeting a diverse group of respondents, including businessmen, CPEC officials, and representatives from the Ministry of Finance, Pakistan Environmental Protection Agency, and Ministry of Planning and Development. Partial Least Squares analysis was employed to test the proposed relationships and hypotheses. The results indicate a significant positive impact of green finance on the development of sustainable infrastructure and the innovation of green technology. Additionally, the results underscore the pivotal role of environmentally friendly technologies and sustainable infrastructure in driving the achievement of SDGs, especially in the social, economic, and environmental dimensions. The study findings offer actionable insights for policymakers, highlighting the critical need to integrate green finance with sustainable practices to foster economic growth and environmental protection. These findings provide a strategic roadmap for nations aiming to align their development goals with global sustainability standards.

Green building (GB) techniques are essential for reducing energy waste in the construction sector, which accounts for almost 40% of global energy consumption. Despite their importance, challenges such as occupant behavior and energy management gaps often result in GBs consuming up to 2.5 times more energy than intended. To address this, Building Automation Systems (BAS) play a crucial role in enhancing energy efficiency. This research develops a predictive model for GB design using machine learning to minimize energy consumption and improve indoor sustainability. The dataset is utilized to predict cooling and heating individually, with data visualization by graphically illustrating dataset features and preprocessing through Z-Score normalization and dataset splitting. The proposed model, based on active learning and utilizing ML regressors such as Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), CatBoost (CB), Light Gradient Boosting Machine (LGBM), K-Nearest Neighbor (KNN), and Logistic Regressor (LR), shows significant performance improvements. The CBR-AL model achieves impressive results with values of 0.9975 for cooling (Y1) and 0.9883 for heating (Y2), indicating a high level of accuracy. The model’s success in reducing energy consumption and improving sustainability has potential ripple effects, including substantial cost savings, reduced carbon footprints, and improved operational efficiency in green buildings. This approach not only enhances environmental sustainability but also sets a benchmark for future advancements in predictive modelling for energy management.

Sustainable tourism development (STD) has been much touted in academia and policy circles; however, developing economies are still struggling to adopt STD. This phenomenon motivated us to trace the barriers hindering STD and their interrelationships in the context of developing economies. This study identified barriers through a systematic literature review and expert opinions. A six-tier hierarchical structure was developed using interpretive structural modeling (ISM). The Matrice d’Impacts croisés multiplication appliquée á un classement (MICMAC) was applied to assess the driving and dependence power. The findings illustrate that all barriers are significant and influence STD; however, the most critical are ‘lack of government interest’, ‘funding constraints’, and ‘lack of integration among institutions and public–private people partnerships (4P)’. The findings of this research will be helpful for managers and policymakers to comprehend the driving and dependent power and categorization of barriers and devise pragmatic strategies to mitigate the barriers and promote STD.

A green building (GB) is a design idea that integrates environmentally conscious technology and sustainable procedures throughout the building’s life cycle. However, because different green requirements and performances are integrated into the building design, the GB design procedure typically takes longer than conventional structures. Machine learning (ML) and other advanced artificial intelligence (AI), such as DL techniques, are frequently utilized to assist designers in completing their work more quickly and precisely. Therefore, this study aims to develop a GB design predictive model utilizing ML and DL techniques to optimize resource consumption, improve occupant comfort, and lessen the environmental effect of the built environment of the GB design process. A dataset ASHARE-884 is applied to the suggested models. An Exploratory Data Analysis (EDA) is applied, which involves cleaning, sorting, and converting the category data into numerical values utilizing label encoding. In data preprocessing, the Z-Score normalization technique is applied to normalize the data. After data analysis and preprocessing, preprocessed data is used as input for Machine learning (ML) such as RF, DT, and Extreme GB, and Stacking and Deep Learning (DL) such as GNN, LSTM, and RNN techniques for green building design to enhance environmental sustainability by addressing different criteria of the GB design process. The performance of the proposed models is assessed using different evaluation metrics such as accuracy, precision, recall and F1-score. The experiment results indicate that the proposed GNN and LSTM models function more accurately and efficiently than conventional DL techniques for environmental sustainability in green buildings.