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Climate change has aroused widespread concern around the world, which is one of the most complex challenges encountered by human beings. The underlying cause of climate change is the increase of carbon emissions. To reduce carbon emissions, the analysis of the factors affecting this type of emission is of practical significance.

This paper identified five factors affecting carbon emissions using the logarithmic mean Divisia index (LMDI) decomposition model (e.g. per capita carbon emissions, industrial structure, energy intensity, energy structure and per capita GDP). Besides, based on the projection pursuit method, this paper obtained the optimal projection directions of five influencing factors in 30 provinces (except for Tibet). Based on the data from 2000 to 2014, the authors predicted the optimal projection directions in the next six years under the Markov transfer matrix.

The results indicated that per capita GDP was the critical factor for reducing carbon emissions. The industrial structure and population intensified carbon emissions. The energy structure had seldom impacted on carbon emissions. The energy intensity obviously inhibited carbon emissions. The best optimal projection direction of each index in the next six years remained stable. Finally, this paper proposed the policy implications.

This paper provides an insight into the current state and the future changes in carbon emissions.

The numerous spoil grounds brought about by mega transportation infrastructure projects which can be influenced by the ecological environment. To achieve better management of spoil grounds, this paper aims to assess their comprehensive risk levels and categorize them into different categories based on ecological environmental risks.

Based on analysis of the environmental characteristics of spoil grounds, this paper first comprehensively identified the ecological environmental risk factors and developed a risk assessment index system to quantitatively describe the comprehensive risk levels. Second, this paper proposed a comprehensive model to determine the risk assessment and categorization of spoil ground group in mega projects integrating improved projection pursuit clustering (PPC) method and K-means clustering algorithm. Finally, a case study of a spoil ground group (includes 50 spoil grounds) in a mega infrastructure project in western China is presented to demonstrate and validate the proposed method.

The results show that our proposed comprehensive model can efficiently assess and categorize the spoil grounds in the group based on their comprehensive ecological environmental risk. In addition, during the process of risk assessment and categorization of spoil grounds, it is necessary to distinguish between sensitive factors and nonsensitive factors. The differences between different categories of spoil grounds can be recognized based on nonsensitive factors, and high-risk spoil grounds which need to be focused more on can be identified according to sensitive factors.

This paper develops a comprehensive model of risk assessment and categorization of a group of spoil grounds based on their ecological environmental risks, which can provide a reference for the management of spoil grounds in mega projects.

A core challenge of assessing regional agricultural drought vulnerability (RADV) is to reveal what vulnerability factors, under which kinds of synergistic combinations and at what strengths, will lead to higher vulnerability: namely, the influence patterns of RADV.

A two-phased grey rough combined model is proposed to identify influence patterns of RADV from a new perspective of learning and mining historical cases. The grey entropy weight clustering with double base points is proposed to assess degrees of RADV. The simplest decision rules that reflect the complex synergistic relationships between RADV and its influencing factors are extracted using the rough set approach.

The results exemplified by China's Henan Province in the years 2008–2016 show higher degrees of RADV in the north and west regions of the province, in comparison with the south and east. In the patterns with higher RADV, the higher proportion of agricultural population appears in all decision rules as a core feature. A smaller quantity of water resources per unit of cultivated land area and a lower adaptive capacity, involving levels of irrigation technology and economic development, present a significant synergistic influence relationship that distinguishes the features of higher vulnerability from those of the lower.

The proposed grey rough combined model not only evaluates temporal dynamics and spatial differences of RADV but also extracts the decision rules between RADV and its influencing factors. The identified influence patterns inspire managerial implications for preventing and reducing agricultural drought through its historical evolution and formation mechanism.

The purpose of this paper is to analyze significant determinants to assess the probability of insureds’ intent to buy (ITB) insurance and willingness to pay (WTP) quantum of dollars for security benefits.

The authors use the Double Hurdle Model (DHM) and Neural Network (NN) architecture to analyze the insureds’ behavior for ITB and WTP. The authors apply these frameworks to all the 503 insureds of a branch of a leading insurer in the United Arab Emirates.

The DHM identified age, loans & liabilities, body mass index, travel outside the UAE, salary and country of origin (Middle Eastern and African) as significant determinants to predict WTP for social security benefits. In addition to these determinants, NN architecture identified insurance replacement, holding multiple citizenship, age of parents, mortgages, country of origin: Americas, length of travel, income of previous year and medical conditions of insured as additional important determinants to predict WTP for social security benefits; thus, NN is found to be superior to DHM due to its lowest RMSE and AIC in the holdout sample and also its flexibility and no assumptions unlike econometric models.

Insureds’ data used from one UAE Branch limit the generalizability of empirical findings.

The study findings will enable the insurers to appropriately design the insurance products that match the insurers’ behavior of ITB and WTP for social security benefits.

The study findings have the potential for insurance institutions to be more flexible in their insurance practices through public–private partnerships.

This is the authors’ original research work.

This study uses a meta-analysis approach to analyse the impact of applying corporate green accounting practices as vital sustainable development tools on firm performance. This study aims to examine the moderating effects of country-specific variables and characteristics on the association between corporate green accounting and firm performance.

Three databases were used for a meta-analysis of 68 independent studies involving 19,625 subjects conducted over 25 years from 1996 to 2020.

The results show that corporate green accounting positively affects firm performance, but country-specific variables do not moderate this association. The positive association between corporate green accounting and firm performance was enhanced when it was measured in terms of environmental costs. Subgroup analyses revealed that study characteristics are significant source of heterogeneity in the corporate green accounting indicators-firm performance association.

The findings suggest that firms should strategise to integrate environmental costs into their respective financial accounting frameworks, which would help managers justify the contribution of their firms towards environmental protection.

Accessing accurate and timely information on corporate environmental functioning can assist national policymakers in framing appropriate legislation on environmental protection and sustainable development.

Although meta-analysis has been used previously in accounting research (Guthrie and Murthy, 2009; Alcouffe et al., 2019), to the best of the authors’ knowledge, this is the first study to use a meta-analytical technique to examine the impact of corporate green accounting on firm performance.

With the prosperity of grey extension models, the form and structure of grey forecasting models tend to be complicated. How to select the appropriate model structure according to the data characteristics has become an important topic. The purpose of this paper is to design a structure selection method for the grey multivariate model.

The linear correction term is introduced into the grey model, then the nonhomogeneous grey multivariable model with convolution integral [NGMC(1,N)] is proposed. Then, by incorporating the least absolute shrinkage and selection operator (LASSO), the model parameters are compressed and estimated based on the least angle regression (LARS) algorithm.

By adjusting the values of the parameters, the NGMC(1,N) model can derive various structures of grey models, which shows the structural adaptability of the NGMC(1,N) model. Based on the geometric interpretation of the LASSO method, the structure selection of the grey model can be transformed into sparse parameter estimation, and the structure selection can be realized by LASSO estimation.

This paper not only provides an effective method to identify the key factors of the agricultural drought vulnerability, but also presents a practical model to predict the agricultural drought vulnerability.

Based on the LASSO method, a structure selection algorithm for the NGMC(1,N) model is designed, and the structure selection method is applied to the vulnerability prediction of agricultural drought in Puyang City, Henan Province.

Today, with the increasing involvement of the environment and human beings business units, paying attention to fulfilling social responsibility obligations while making a profit has become increasingly necessary for achieving sustainable development goals. Attention to profit by organizations should not be without regard to their social and environmental performance. Social responsibility accounting (SRA) is an approach that can pay more attention to the social and environmental performance of companies, but it has many barriers. Therefore, the purpose of this study is to identify barriers to SRA implementation and provide strategies to overcome these barriers.

In this study, the authors identify barriers to social responsibility accounting implementation and provide strategies to overcome these barriers. By literature review, 12 barriers and seven strategies were identified and approved using the opinions of six academic experts. Interpretive structural modeling (ISM) has been used to identify significant barriers and find textual relationships between them. The fuzzy technique for order performance by similarity to ideal solution (TOPSIS) method has been used to identify and rank strategies for overcoming these barriers. This study was undertaken in Iran (an emerging market). The data has been gathered from 18 experts selected using purposive sampling and included CEOs of the organization, senior accountants and active researchers well familiar with the field of social responsibility accounting.

Based on the results of this study, the cultural differences barrier was introduced as the primary and underlying barrier of the social responsibility accounting barriers model. At the next level, barriers such as “lack of public awareness of the importance of social responsibility accounting, lack of social responsibility accounting implementation regulations and organization size” are significant barriers to social responsibility accounting implementation. Removing these barriers will help remove other barriers in this direction. In addition, the results of the TOPSIS method showed that “mandatory regulations, the introduction of guidelines and social responsibility accounting standards,” “regulatory developments and government incentive schemes to implement social responsibility accounting,” as well as “increasing public awareness of the benefits of social responsibility accounting” are some of the essential social responsibility accounting implementation strategies.

The findings of the study have implications for both professional accounting bodies for developing the necessary standards and for policymakers for adopting policies that facilitate the implementation of social responsibility accounting to achieve sustainability.

This paper creates a new perspective on the practical implementation of social responsibility accounting, closely related to improving environmental performance and increasing social welfare through improving sustainability.

Experts believe that the strategies mentioned above will be very effective and helpful in removing the barriers of the lower level of the model. To the best of the authors’ knowledge, for the first time, this study develops a model of social responsibility accounting barriers and ranks the most critical implementation strategies.

The purpose of this paper is to put forward a multi-stage information aggregation method based on grey inspiriting control lines to evaluate the objects dynamically and comprehensively.

According to the evaluation value of the objects, the positive and negative incentive lines were set up and the predicted values were solved based on the grey GM(1, 1) model, so the value with expected information could be evaluated. In the evaluation, the part above the positive incentive line should be “rewarded” and that below the negative incentive line should be “punished” appropriately. Thereby the double incentive effects of “the current development situation and future development trend” to objects could be implemented on the basis of control.

This method can primarily describe the decision maker's expectancy of the development of evaluation objects and make the evaluation results have better practical application value.

Many comprehensive evaluations were always based on the past information. However, the future development trend of the evaluated object is also very important. This study can be used in the evaluation for future application and development.

The paper succeeds in providing not only a method of multi-phase information aggregation with expectancy information, but also a simple and convenient method solving nonlinear inspiring lines objectively.

This paper aims to analyze the mechanism underlying the impact of boundary-spanning search (BS) on the sustainable development ability (SDA) of service-oriented manufacturing enterprises and to emphasize the intermediary role of knowledge integration (KI). The moderating role of knowledge inertia on the link between BS and KI is also investigated.

This study constructs direct, mediating and moderating effects, selects 110 service-oriented manufacturing enterprises as research samples and obtains empirical data from questionnaires and annual reports. Among them, triangulation is skilfully used to obtain questionnaire data, and the regression method is used to test model relationships.

The results show that BS not only directly enhances SDA but also indirectly affects it through KI, which plays a mediating role in the impact of BS on SDA. In addition, knowledge inertia negatively moderates the relationship between BS and KI.

This paper makes three contributions. First, it enriches the research on the antecedent variables related to the SDA of service-oriented manufacturing enterprises. Second, by examining the mediating role of KI and the moderating role of knowledge inertia, the relationship between BS and SDA is revealed. Third, the research on knowledge management related to the SDA of service-oriented manufacturing enterprises is expanded.

The purpose of this paper is to design an innovative performance modeling system by jointly using data envelopment analysis (DEA) and artificial neural network (ANN). The hybrid DEA-ANN model integrates performance measurement and prediction frameworks and serves as an adaptive decision support tool in pursuit of best performance benchmarking and stepwise improvement.

Advantages of combining DEA and ANN methods into an optimal performance prediction model are explored. DEA is used as a preprocessor to measure relative performance of decision-making units (DMUs) and to generate test inputs for subsequent ANN prediction modules. For this sequential process, Charnes, Cooper, and Rhodes and Banker, Chames and Cooper DEA models and back propagation neural network (BPNN) are used. The proposed methodology is empirically supported using longitudinal data of Japanese electronics manufacturing firms.

The combined modeling approach proves effective through sequential processes by streamlining DEA analysis and BPNN predictions. The DEA model captures notable characteristics and efficiency trends of the Japanese electronics manufacturing industry and extends its utility as a preprocessor to neural network prediction modules. BPNN, in conjunction with DEA, demonstrates promising estimation capability in predicting efficiency scores and best performance benchmarks for DMUs under evaluation.

Integration of adaptive prediction capacity into the measurement model is a practical necessity in the benchmarking arena. The proposed framework has the potential to recalibrate benchmarks for firms through longitudinal data analysis.

This research paper proposes an innovative approach of performance measurement and prediction in line with superiority-driven best performance modeling. Adaptive prediction capabilities embedded in the proposed model enhances managerial flexibilities in setting performance goals and monitoring progress during pursuit of improvement initiatives. This paper fills the research void through methodological breakthrough and the resulting model can serve as an adaptive decision support system.