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Consumers' willingness to pay premium (WTPP) for two different types of agricultural brand labels (enterprise and regional), are evaluated through a non-hypothetical Random n-price auction experiment during the online purchase of fresh agricultural products. The purpose of this paper is to evaluate the two WTPP, compare their differences, and explore their sustainability.

Data were collected in July–August 2020 from a sample of 310 consumers in Liaoning Province, China. A nonhypothetical random n-price auction experiment was implemented in a simulated online shopping environment.

The results show that WTPP exists, and WTPP level of regional brand labels is higher than that of enterprise brand labels. Consumers' WTPP is sustainable. Consumers with low WTPP for enterprise brand labels and consumers with high WTPP for regional brand labels have stronger willingness to repurchase.

The results have direct practical implications for developing brand agriculture and encouraging “brand consumption”. The results can provide theoretical reference for policymakers, enlightenment for the development and effective dissemination of agricultural brand labels and important information to e-retailers on how to sale agricultural products with agricultural brand labels.

To the best of the authors' knowledge, no previous study has related WTPP and its sustainability for agricultural brand labels in China. We try to fill a gap in literature on consumers' WTPP for agricultural brand labels. And the authors explore the sustainability of WTPP by analyzing the impact of WTPP on repurchase intention and recommendation intention respectively.

Transitioning to a low-carbon economy requires a positive response by society, including business organizations, towards the green concept and also requires the implementation of long-term green strategies. These requirements could impose various transition risks on the sustainable development of the firms; hence, the present study aims to examine the impact of climate transition risk on a firm’s financial performance and market value creation from the Indian perspective.

We have considered the firm-level environmental risk score (ERS) to evaluate the sensitivity of a firm’s profitability (measured by ROA & ROE) and market value (measured by Tobin’s Q) towards the climate transition risk. The present study used multiple regression analysis to examine the impact of climate transition risk on the firm’s financial performance and market value creation, as evidenced by Nifty 50 companies.

The empirical results suggested that corporate climate transition risks have been positively associated with the firm’s financial performance indicators but negatively impacted the firm’s market value creation in the case of select Indian-listed firms. Hence, our results indicate that with the increase of firm-level climate transition risk, the firm’s financial performance increases but negatively affects the firm’s market value creation. The robustness tests have also confirmed the same results and supported our analysis.

The present paper contributes to the existing literature on climate risks and firms’ performance by providing insights about firms’ sensitivity towards climate transition risk from the Indian perspective.

The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO₂ emissions, farming areas and the labor force in China.

This analysis applied the autoregressive distributed lag-bound testing (ARDL) approach, Granger causality method and Johansen co-integration test to predict long-term co-integration and relation between variables. Four machine learning methods are used for prediction of the accuracy of climate effect on fruit production.

The Johansen test findings have shown that the fruit crop growth, energy use, CO₂ emissions, harvested land and labor force have a long-term co-integration relation. The outcome of the long-term use of CO₂ emission and rural population has a negative influence on fruit crops. The energy consumption, harvested area, total fruit yield and agriculture labor force have a positive influence on six fruit crops. The long-run relationships reveal that a 1% increase in rural population and CO₂ will decrease fruit crop production by −0.59 and −1.97. The energy consumption, fruit harvested area, total fruit yield and agriculture labor force will increase fruit crop production by 0.17%, 1.52%, 1.80% and 4.33%, respectively. Furthermore, uni-directional causality is correlated with the growth of fruit crops and energy consumption. Also, the results indicate that the bi-directional causality impact varies from CO₂ emissions to agricultural areas to fruit crops.

This study also fills the literature gap in implementing ARDL for agricultural fruits of China, used machine learning methods to examine the impact of climate change and to explore this important issue.