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The purpose of this paper is to quantitatively measure the vulnerability level of the whole rural social-ecological system in Yunnan Province and to analyze the spatial differences of the vulnerability in different regions.

Based on the “exposure-sensitivity-adaptability” vulnerability assessment framework, this paper establishes the index system of rural social-ecological system vulnerability to climate change. Combined with the questionnaire survey and meteorological data, the entropy method was used to measure and analyze the vulnerability level and influencing factors of the overall rural social-ecological system in Yunnan Province. At the same time, the vulnerability level of social-ecological system in Yunnan Province is divided into five levels, and the spatial differences of vulnerability level of 16 states (cities) in Yunnan Province are analyzed.

The results show that: the social-ecological system has high exposure to climate change (0.809), strong sensitivity (0.729), moderate adaptability (0.297) and overall system vulnerability is at a medium level (0.373). Yunnan Province is divided into five levels of social-ecological system vulnerable areas. The areas of extreme, severe, moderate, mild and slight vulnerability account for 21.45%, 24.65%, 36.82%, 13.18% and 3.90% of the whole province, respectively. The geographical division and vulnerability division of Yunnan Province are basically consistent in space.

Comprehensive evaluation of the vulnerability of the social-ecological system of Yunnan Province to climate change is the scientific basis for the country to formulate countermeasures against climate change, and it is also the need to improve the adaptability of the social and economic system of the fragile area, reduce the vulnerability and realize the sustainable development of national social economy. The research results can provide a basis for decision-making of climate adaptation in Yunnan and other regions and provide methods and indicators for the assessment of social-ecological system vulnerability under the background of climate change.

This study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.

Several econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.

The results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.

This study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.

To the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.