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Global climate change characterized by an increase in temperature has become the focus of attention all over the world. China is a sensitive and significant area of global climate change. This paper specifically aims to examine the association between agricultural productivity and the climate change by using China’s provincial agricultural input–output data from 2000 to 2019 and the climatic data of the ground meteorological stations.

The authors used the three-stage spatial Durbin model (SDM) model and entropy method for analysis of collected data; further, the authors also empirically tested the climate change marginal effect on agricultural productivity by using ordinary least square and SDM approaches.

The results revealed that climate change has a significant negative effect on agricultural productivity, which showed significance in robustness tests, including index replacement, quantile regression and tail reduction. The results of this study also indicated that by subdividing the climatic factors, annual precipitation had no significant impact on the growth of agricultural productivity; further, other climatic variables, including wind speed and temperature, had a substantial adverse effect on agricultural productivity. The heterogeneity test showed that climatic changes ominously hinder agricultural productivity growth only in the western region of China, and in the eastern and central regions, climate change had no effect.

The findings of this study highlight the importance of various social connections of farm households in designing policies to improve their responses to climate change and expand land productivity in different regions. The study also provides a hypothetical approach to prioritize developing regions that need proper attention to improve crop productivity.

The paper explores the impact of climate change on agricultural productivity by using the climatic data of China. Empirical evidence previously missing in the body of knowledge will support governments and researchers to establish a mechanism to improve climate change mitigation tools in China.

The purpose of this paper is to show that the environmental income drives economic growth of a large open country.

The authors detect that the relative environmental income has double effect of “conspicuous consumption” on the international renewable resource stock changes when a new social norm shapes to environmental-friendly behaviors by using normal macroeconomic approaches.

Every unit of extra demand for renewable resource consumption increases the net premium of domestic capital asset. Even if the technology spillovers are inefficient to the substitution of capital to labor force in a real business cycle, the relative income with scale effect increases drives savings to investment. In this case, the renewable resource consumption promotes both the reproduction to a higher level and saving the potential cost of environmental improvement. Even if without scale effects, the loss of technology inefficient can be compensated by net positive consumption externality for economic growth in a sustainable manner.

It implies how to earn the environment income determines the future pathway of China’s rural conversion to the era of eco-urbanization.

We test the tax incidence to demonstrate an experimental taxation for environmental improvement ultimately burdens on international consumption side.

Data service (DS) is a special software service that enables data access in cloud environment and provides a unified data model for cross-origination data integration and data sharing. The purpose of the work is to automatically compose DSs and quickly generate data view to satisfy users' various data requirements (DRs).

The paper proposes an automatic DS composition and view generation approach. DSs are organized into DS dependence graph (DSDG) based on their inherent dependences, and DSs can be automatically composed using the DSDG according to user's DRs. Then, data view will be generated by interpreting the composed DS.

Experimental results with real cross-origination data sets show the proposed approaches have high efficiency and good quality for DS composition and view generation.

The authors propose a DS composition algorithm and a data view generation algorithm according to users' DRs.

The purpose of this paper is to comprehensively examine the influence of formal and informal institutional differences on enterprise investment margin, mode and result.

This paper is based on 2,440 micro samples of large-scale outbound investment from 609 Chinese enterprises from the years 2005 to 2016.

The study has found that formal institutional differences have little impact on investment scale, but significantly affect investment diversification. In order to avoid the management risks brought by formal institutional differences, enterprises tend to a full ownership structure. However, the choice between greenfield investment and cross-border mergers and acquisitions is not affected by formal institutional differences. In contrast, the impact of informal institutional differences is more extensive. Both formal and informal institutional differences significantly increase the probability of investment failure. Further research found that the Belt and Road Initiative (BRI) bridges the formal institutional differences.

The study concludes that developing the BRI, especially cultural exchanges with countries alongside the Belt and Road, will help enterprises to “go global” faster and better.

This study aims to examine the effects of occupational characteristics on the length of time required to socialize newcomers. The authors examine task mastery, role clarity and social acceptance as indicators of socialization.

To test the hypotheses, the authors used occupational data from the Bureau of Labor Statistics and survey data of subject matter experts in 35 occupations.

Findings show that occupational differences account for a significant variance in the time needed to socialize newcomers. Across occupations, it takes longer to achieve task mastery than role clarity or social acceptance. Occupational complexity increases the time it takes for newcomers to attain task mastery, role clarity and social acceptance. Additionally, unstructured work and decision-making freedom increase the time it takes for newcomers to attain role clarity.

This study provides both theoretical and empirical guidance on the duration of the organizational socialization period. The study also provides empirical support for prior propositions that different types of newcomer learning occur at different rates.

This paper aims to investigate if and to what extent environmental, social and governance (ESG) practices are influenced by innovation, measured by investment in research and development (R&D) and the number of patents developed by companies.

To test this hypothesis, the authors estimated a regression model for the panel data considering a time horizon of eight years. The analysis was conducted on a sample of listed firms operating in the industrial sector in France, Germany, Italy, Spain, the UK and the USA.

The empirical analysis shows that there is a positive and significant relationship between ESG practices and innovation. Companies investing more in R&D and patents have better ESG performance.

This study contributes to the existing literature by improving the understanding of the importance of innovation in improving ESG practices for firms in the industrial sector. Furthermore, it provides empirical evidence of the ability of innovation to be a valuable tool for sustainable industry development through R&D investment and patent development.

This paper studies the lateral stability regulation of intelligent electric vehicle (EV) based on model predictive control (MPC) algorithm.

Firstly, the bicycle model is adopted in the system modelling process. To improve the accuracy, the lateral stiffness of front and rear tire is estimated using the real-time yaw rate acceleration and lateral acceleration of the vehicle based on the vehicle dynamics. Then the constraint of input and output in the model predictive controller is designed. Soft constraints on the lateral speed of the vehicle are designed to guarantee the solved persistent feasibility and enforce the vehicle’s sideslip angle within a safety range.

The simulation results show that the proposed lateral stability controller based on the MPC algorithm can improve the handling and stability performance of the vehicle under complex working conditions.

The MPC schema and the objective function are established. The integrated active front steering/direct yaw moments control strategy is simultaneously adopted in the model. The vehicle’s sideslip angle is chosen as the constraint and is controlled in stable range. The online estimation of tire stiffness is performed. The vehicle’s lateral acceleration and the yaw rate acceleration are modelled into the two-degree-of-freedom equation to solve the tire cornering stiffness in real time. This can ensure the accuracy of model.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

The paper aims to investigate the possible changes in mean temperature in the Mongolian Plateau associated with the 1.5 and 2°C global warming targets and how snow changes in the Mongolian Plateau when the mean global warming is well below 2°C or limited to 1.5°C.

In total, 30 model simulations of consecutive temperature and precipitation days from Coupled Model Inter-comparison Project Phase 5 (CMIP5) are assessed in comparison with the 111 meteorological monitoring stations from 1961–2005. Multi-model ensemble and model relative error were used to evaluate the performance of CMIP5 models. Slope and the Mann–Kendall test were used to analyze the magnitude of the trends and evaluate the significance of trends of snow depth (SD) from 1981 to 2014 in the Mongolian Plateau.

Some models perform well, even better than the majority (80%) of the models over the Mongolian Plateau, particularly HadGEM2-CC, CMCC-CM, BNU-ESM and GFDL-ESM2M, which simulate best in consecutive dry days (CDD), consecutive wet days (CWD), cold spell duration indicator (CSDI) and warm spell duration indicator (WSDI), respectively. Emphasis zones of WSDI on SD were deeply analysed in the 1.5 and 2 °C global warming period above pre-industrial conditions, because it alone has a significant negative relation with SD among the four indices. It is warmer than before in the Mongolian Plateau, particularly in the southern part of the Mongolian Plateau, indicating less SD.

Providing climate extremes and SD data sets with different spatial-temporal scales over the Mongolian Plateau. Zoning SD potential risk areas and proposing adaptations to promote regional sustainable development.

This paper aims to analyze the impact of Covid-19 on the stock market volatility and uncertainty during the first and second waves.

This study has applied event study and autoregressive integrated moving average models using daily data of confirmed and death cases of Covid-19, US S&P 500, volatility index, economic policy uncertainty and S&P 500 of Bombay Stock Exchange to attain the purpose.

It is observed that, during the first wave, the confirmed cases and the fiscal measure have a significant impact, while the vaccination initiative and the abnormal hike of confirmed cases have a significant impact on the US stock returns during the second wave. It is further observed that the volatility of Indian and US stock markets spillovers during the sample period. Moreover, a perpetual correlation between the Covid-19 and the stock market variables has been noticed.

At present, the world is experiencing the third wave of Covid-19. This paper has considered the first and second waves.

It is expected that business leaders, stock market regulators and the policymakers will be highly benefitted from the research outcomes of this study.

This paper briefly highlights the drawbacks of existing policies and suggests appropriate guidelines to successfully implement the forthcoming initiatives to reduce the catastrophic impact of Covid-19 on the stock market volatility and uncertainty.