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Alkaline-earth metal elements in the s-block of the periodic table have rarely been studied as active sites for nitrogen (N2) photofixation.

The conversion of N2 and CO2 into urea through a photocatalytic C–N coupling reaction under ambient conditions serves as a novel green avenue for urea synthesis.

In the process of responding to major public health emergencies, the transformation of emergency scientific research results often faces many unfavourable factors such as limited resources, tight time, changes in needs, and lack of results. It is necessary to evaluate and analyze the ability to transform emergency scientific research results under public health emergencies, so as to rationally allocate emergency scientific research resources between subjects and regions, improve the efficiency of emergency results transformation, enhance emergency scientific research capabilities, and efficiently support incident prevention, control, and treatment. Starting from the patent level, this paper constructs an indicator system to evaluate the transformation ability of emergency scientific research results under major public health emergencies. It improves the minimum distance-maximum entropy combination weighting method to realize the static evaluation of transformation ability for emergency scientific research results from the perspective of patents, then constructs the dynamic evaluation model of transformation ability for emergency scientific research results in public health emergencies from the perspective of patents, and carries out the dynamic evaluation of the emergency scientific research achievements transformation ability of different subjects and different regions. We also improve the ER index, measure the static polarization effect of the transformation ability for regional emergency scientific research results, and consider the time factor to construct a dynamic polarization effect measurement model for the transformation ability of emergency scientific research achievement. Furthermore, this paper improves the measurement model of contribution degree to the polarization effect, and analyzes the contribution degree to polarization of the transformation ability for regional emergency scientific research results.

Purpose This study aims to examine the impact of corporate social responsibility (CSR) awards on firms’ market value considering these awards as a signal and proxy for the effectiveness of CSR practice. Design/methodology/approach There are 342 announcements of CSR awards in China from 2006 to 2017 screened and analyzed using the event study methodology. Findings The stock market reacts significantly negatively to CSR award announcements in the short term. Firms that are state-owned, belong to the manufacturing industry, outside east China, repeatedly win awards and are listed in the Chinese H-share market, experience a stronger stock market reaction. Interestingly, the long-term stock returns of award winners are significantly positive for multiyear holding periods. Practical implications The findings offer stakeholders clear guidelines on how to manage communications in the market to extract enhanced financial performance from CSR award announcements. Originality/value This study chooses CSR awards as a proxy for the effectiveness of excellent CSR practice. This study also contributes to the CSR literature by analyzing how investors use the award information to make investment decisions.

Quality has always been an important factor affecting the competitive advantages and financial performance of firms. Some research has examined the impacts of Malcolm Baldrige Award and European Qu...

PurposeThe sustainable development of the energy–economy–environment (3E) needs to ensure the balanced interplay between the energy–economy and the environment. Likewise, creating such balance has become a critical policy issue among countries worldwide. However, in the past, studies have ignored to create a balanced interplay of the energy–economy and environment. Therefore, to address said research gap, this paper aims to develop a graph model for conflict resolution (GMCR)-based strategy for the sustainable development of the 3E to ensure better environmental quality.Design/methodology/approachTo develop a strategic framework for the balanced interplay between the energy–economy and the environment, the study used the GMCR approach and designed a mechanism for the sustainable development of the 3E to ensure better environmental quality in the context of Pakistan.FindingsThe results from the GMCR indicate that sustainable development of the 3E to ensure better environmental quality is possible when government focuses on the sustainable growth of the economy through environmental policies and the use of renewable energy.Practical implicationsBy solving the 3E conflict, this study provides policy insights for the government of Pakistan for the sustainable development of the energy economy and the environment to ensure better environmental quality in the country.Originality/valueFor the first time, this paper tried to put forward a way through which conflicting objectives can be achieved together for the sustainable development of the 3E to ensure better environmental quality.

Around the globe, all developing and developed economies focus on renewable energy sources for clean and sustainable economic growth and aim to decrease carbon emission to improve the climatic condition. This study aims to evaluate the feasible renewable energy source in Pakistan and study the criteria based on the investigation of wind energy, biomass energy, geothermal and solar energy, and their effects on Pakistan’s climate. As renewable energy, clean and sustainable production is only produced from renewable energy sources; the current paper bears the viability of five primary renewable energy sources in Pakistan. This article established an advanced fuzzy (AHP) with a multi-perspective approach based on the expert assessment to select the efficient renewable energy in Pakistan. Further in this study, we utilized the DEA model to investigate the relative efficacy of renewable energy. This study indicated that wind energy is the efficient renewable energy source in Pakistan, which showed an efficiency score of 0. 1000. Our results further suggested that wind energy’s next option for renewable energy solar best for renewable production in Pakistan. The results further exposed that the solar is the second-highest energy source with an efficiency score of 0. 777. Moreover, geothermal gets a less efficient score, that of 0.198. Further, our study showed that the economic benefits (EBs) and environmental implication criteria are significant in this research. This article gives a strong recommendation for a future wind energy source in Pakistan. Our results suggest that the best renewable energy source in the future is wind energy for the sustainable and economic development of Pakistan and other developing countries.

Based on the data of Chinese listed family firms from 2008 to 2016, we investigate the impact of family involvement on firm innovation and the moderating effect of family member composition. The results show that increased family involvement significantly reduces R&D investment intensity and the number of patent applications. With the increased richness of the kinship of family members involved in management, the negative impact of family involvement on patent applications is weakened, but family member composition does not have a significant moderating effect on the relationship between family involvement and R&D investment intensity. Further analysis shows that the number of invention patent applications decreases as the degree of family involvement increases, but family involvement has no significant effect on utility model patent and design patent applications. Family member composition has a significant moderating effect on the relationship between family involvement and invention patent applications. The results have value as a reference for exploring how family involvement affects firm innovation and can also help the actual controller to take effective measures to optimize family member composition and improve the innovation performance of family firms.

The collaborative filtering adopted by traditional recommendation system has data sparsity problem, and the matrix decomposition method simply decomposes users and items into linear models for potential factors. These limitations have led to limited effectiveness for traditional recommendation algorithms. In this case, the recommendation system based on deep learning has emerged. Most of the current deep learning recommendations use deep neural networks to model some basic information, and in the modeling process, according to the input data categories, multiple mapping paths are used to map the original data to the potential vector space. However, these recommendations ignore that the alliance between different categories may have a potential impact on the recommendation effect. Aiming at this problem, this paper proposes a feedforward deep neural network of multi-form category features combination for recommendation, which is deep alliance neural network. According to the different alliance modes, it can be divided into deep series network (DSN), deep parallel network (DPN) and deep random network (DRN), which are used to solve the recommendation problem of implicit feedback. At the same time, a fusion model SMLP based on deep alliance neural network and traditional Multi-Layer Perceptron (MLP) is proposed to try to explore the performance of the fusion model. Finally, experiments on public datasets show that our proposed method significantly improves the existing methods. Empirical evidence indicates that deep series network and deep parallel network can provide better recommendation performance, while the recommended performance of deep random network and fusion model SMLP is not ideal. This indicates that the deep alliance network needs to pay special attention to the order of the category features in the process of category features association.

Education is the cultivation of people to promote and guarantee the development of society. Education reforms can play a vital role in the development of a country. However, it is crucial to continually monitor the performance of the educational model by forecasting the progress of outcomes. Forecasting models can help to analyze the impact of future outcomes by showing yearly trends. For this study, we developed a hybrid, forecasting time-series model by using seasonal autoregressive integrated moving average (SARIMA) and support vector regression (SVR) to monitor educational reform in Morocco. We analyze the performance of six universities and provide a prediction model to evaluate the performance of the best-performing university after implementation of the latest reform, i.e., from 2015 to 2030. Development of a new prediction model for forecasting the research outcomes of universities.

Although internal evaluation models at research institutions have been developed for comparative research at national and international levels, the quality of inter-institutional systematic research and trend assessment models have not previously been reported in the literature. In this research, a conceptual framework called the Sustainable Research Quality Assessment Model (SRQAM) was developed to evaluate research quality indicators such as the number of publications per year and citations per year in higher education institutions. Subsequently, RQAM was applied to six selected universities in Morocco in order to observe its applicability. SRQAM consists of five steps: Duration determination, selection of research quality indicators such as citations per year (CPY), and trend indicators such as the number of publications (NP), data extraction and statistical analyses. Although paper publication and citations play a vital role in higher education institutions, the literature on publication activities is relatively scarce. Several factors influence the publication and citation rate of the university. Therefore, this article focuses on research quality assessment and discusses publication activities by exploring factors that may increase or decrease the publication and citation rate of higher education institutions. The application of this research model in six institutions from 1996 to 2020 shows that during this period, the highest averages for academic staff (AS), citations per year (CPY) and number of publications (NP) ( $\text{AS}=2004.6+317.3,\ \text{CPY}=5954.6\pm 2642.7,\ \text{NP}=749.1\pm 684.2$ ) was at Mohammed V University, Rabat. SRQAM is a fundamental instrument for institutional research and assessment and can guide research policy.

Poor agricultural communities are particularly more disruptive to changes in climate. In southeast Asian countries, Pakistan is extremely vulnerable to weather-related events including droughts and floods. This research study aims to determine the farmers' perception towards climatic risks, farmers' choice of adaptation strategies and factors influencing farmers' decision of adaptation measures. A face-to-face household survey was conducted to collect primary data of 378 farm households from three tehsils of district Charsadda-Khyber Pakhuntkhwa (KP) province of Pakistan. A structured questionnaire was utilized as an instrument to collect data from the targeted farmers in study areas (three tehsils of district Charsadda). A binary probit model approach was used to assess the major factors affecting farmers' decision towards adaptation measures. Results revealed that changing crop varieties, diversification of crops, changing crop calendar, and insurance of crops were the major adaptive measures exercised by farm households in their farms. Findings of the binary probit approach showed that age of the respondents, farm size, educational level, credit access, household size, extension services access and perception of increased floods, and reduction in precipitation had substantial effect on the farmers' adaptation strategies choice. Advanced agricultural practices in response to the climatic risks can thus have substantial effects and reduction in farmers' exposure to natural calamities. Study findings of our research can guide policy makers and concerned authorities and provide policy implications for future research studies.

Mergers and divestitures are two major economic activities. In this article, an evolutionary model combined with mergers and divestitures is proposed from the perspective of complex network science. More specifically, the Axelrod model is introduced to present the identity of entities on the economic network, the Cobb–Douglas production function is used to calculate the power of each entity, and the probabilities of mergers and divestitures are determined by the Fermi function considering key economic indicators during the evolution. We find that mergers and divestitures have neither good nor bad characteristics by themselves, and that the key to the success of these activities is the ability and the development status of entities in the economic network. Identity dimension, learning ability, initial network size, and density usually play important roles in the progress under the influence of mergers and divestitures. Power, degree, age, identity distance, maximal ingredient, network size, and the frequency of mergers and divestitures likewise can give rise to very different processes in different economic environments. The presented results may have a high reference value when economic sectors develop specific merger and divestiture policies, and we also outline directions for future research that seem important and promising along the same lines.

Sustainable development goals (SDG) involve not only environmental issues but also economic, social, and cultural concerns. Higher education plays a key role in promoting sustainable development initiatives and in empowering people to change their thinking and to strive for a sustainable future. However, the main issue that needs to be presently resolved is how leaders, teachers, and students in higher education can achieve sustainable development in their system vision, mission and values, strategic plans, and organizational culture. Morocco is a country with a long history of higher education and has continuous reforms for sustainable development. In the process of responding to the wave of globalization, the Moroccan government has begun to formulate a higher education reform plan to maintain its competitiveness and achieve the SDG standards. Therefore, this study is focused on the quality of the higher education system through which the sustainability of higher education reform can be implemented. With this in mind, an organized approach that involved a questionnaire using the SWOT (strengths, weaknesses, opportunities, and threats) decision-making model with integration of analytic hierarchy process (AHP) and Entropy method was developed. The questionnaires were filled out by the experts, staff, and students of the higher education system (universities) to obtain the important key factors for the SWOT analysis. The AHP was used for the qualitative analysis of the weights of the SWOT factors, while the Entropy method was applied for the objective analysis of the number of different weight attributes. After integration of AHP with Entropy, the finalized variables were ranked, these results are more reliable and realistic to decision-makers. Finally, the SWOT matrix was established based on the questionnaire assessment and the AHP with Entropy weights to help implement the higher education reform policy and to monitor the quality of the current education system. The results also indicate that higher education reform must incorporate many changes, including effective budget planning, skilled experts, internationalization, improved and expanded infrastructure, reformed study curriculum, and latest training.

A comprehensive assessment of energy use, environmental degradation, and economic progress can play a significant role in transition towards low-carbon economy, and it can serve as a reference for the green economic development for the rest of the developing world. The objective of this paper is to empirically investigate the current status of conventional and renewable energy use and environmental degradation. Following this, we have analyzed the decoupling relation among environmental degradation, energy use, and economic progress in Pakistan. The study adopted the comprehensive data from year 1972–2017 and applied Tapio decoupling method to explore the decoupling status of environmental degradation, energy use, and economic progress in Pakistan. The key finding from the study shows that the overall value of carbon emissions in Pakistan is relatively increasing with the passage of time and shows about 5.26% average growth rate which is creating severe environmental degradation. There were observed several fluctuations in the trend of carbon emissions which is basically due to the policy changes in the country. From the decoupling point of view, we found the decoupling linkage between energy use and carbon emissions that is growth negative decoupling, whereas a weak decoupling relation has been observed among carbon emissions and economic progress which means that in most of the year’s county has achieved more economic growth compared with the carbon emissions. In addition, the similar weak decoupling relationship was found among energy use and economic progress. In the light of these findings, it is suggested to policymakers to promote technological advancement and alternate energy that will not only improve environmental quality, but it will also promote a low-carbon economy.

The demand for primary energy resources has increased significantly due to the rapid growth of the global economy and increasing greenhouse gas (GHG) emissions. Therefore, improving energy efficiency levels is essential for global energy, energy security, and environmental sustainability. In the context of the Asia-Pacific region, the study of energy efficiency among different countries can play a role in better energy utilization. These countries also provide a policy for the Asia-Pacific region to improve its energy utilization. This study’s primary focus is to investigate the optimal efficiency score of 15 areas of the Asia-Pacific region, and the analysis is based on super-efficiency (radical) and super slacks-based measure (SBM) data in a nonparametric DEA model. Three areas in the Asia-Pacific are selected for energy efficiency measures: South Asia, East Asia, and Australasia. The results suggest that Bangladesh, Pakistan, China, Singapore, New Zealand, the Philippines, Japan, India, Indonesia, Malaysia, Thailand, and Vietnam obtain the most efficient score of 1 in both DEA models throughout the study period. Australia and Sri Lanka receive a low score during all study periods, while Hong Kong does not have data for all study years. The results of the study will help improve energy performance, cost-effectiveness, and environmental sustainability, increasing the competitiveness and scalability of efficient energy sources.

This paper focuses on how the development of the strategy map for the Central Bank of Nepal is done by linking the key measures with the four perspectives of Balanced Scorecard perspectives (finance, customer, internal process, and learning and growth). The central bank is a vital part of an economy, and therefore it should not limit its strategy implementation and performance measurement within the financial perspectives only. So, this paper serves the purposes of creating a road map for discovering the key indicators to create the strategy map and to support future research in the Nepalese banking field. In the absence of a previous strategy map, the strategic goals of the bank were derived from the relevant literature, the bank’s vision, mission, goals, and mandates followed by the expert’s selection. A list of the bank’s strategic goals was derived from the survey questionnaire corresponding to the four perspectives of Balanced Scorecard. The measures of perspective were studied by factor analysis. The research therefore uses a descriptive-exploratory method. Finally, the strategy map was developed and the result consisted of the strategic goals and measures in financial, customer, internal process, and learning and growth.

Education is the cultivation of people to promote and guarantee the development of society. Education reforms can play a vital role in the development of a country. However, it is crucial to continually monitor the educational model’s performance by forecasting the outcome’s progress. Machine learning-based models are currently a hot topic in improving the forecasting research area. Forecasting models can help to analyse the impact of future outcomes by showing yearly trends. For this study, we developed a hybrid, forecasting time-series model by long short-term memory (LSTM) network and self-attention mechanism (SAM) to monitor Morocco’s educational reform. We analysed six universities’ performance and provided a prediction model to evaluate the best-performing university’s performance after implementing the latest reform, i.e., from 2015–2030. We forecasted the six universities’ research outcomes and tested our proposed methodology’s accuracy against other time-series models. Results show that our model performs better for predicting research outcomes. The percentage increase in university performance after nine years is discussed to help predict the best-performing university. Our proposed algorithm accuracy and performance are better than other algorithms like LSTM and RNN.

The establishment of Special Economic Zones (SEZs) is a lengthy, expensive, and long-term orientated endeavor. Proper selection of SEZs is indispensable to meet the objectives of export-led growth and value up-gradation. Consideration of sustainability issues in such planning under the Zone 3.0 paradigm is critical to achieve Sustainable Development Goals (SDGs) by 2030. Multiple key factors such as location, linkages, labor force, suitability of industries, incentives and facilitation, and market orientation are important in decision-making process of establishing SEZs. Furthermore, environmental conditions and resource availability need to be considered in the planning and policy making processes to keep symmetry in the natural environment and ecosystem of the areas under consideration for SEZs. The present study uses Multi-Criteria Decision Analysis (MCDA) methods in the perspectives of green industrial zone planning and development in Pakistan under the flagship project of China-Pakistan Economic Corridor (CPEC) of China&rsquo, s Belt and Road Initiative (BRI). This research uses Delphi method, Analytical Hierarchy Process (AHP), and the Fuzzy Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR). The Delphi method has been used to identify the main criteria, sub-criteria, and their weights for 3 SEZs under consideration. The results of AHP analysis unfolded that the majority of the experts believe the location and land aspect is the most pivotal criteria in setting SEZs followed by linkages, subsidies, and facilities criteria. Finally, the results of Fuzzy VIKOR analysis considering environmental sustainability reveals that Faisalabad SEZ is the best suited under given criteria and sub-criteria.