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Henan Province is the birthplace of Chinese civilization and one of the earliest human settlements, which means that the area has an important national cultural heritage. Traditional villages are an important facet of this cultural heritage, and studying their spatiotemporal characteristics in different periods has important theoretical and practical significance for the sustainable development and protection of cultural heritage in the region. This paper takes the traditional villages of the Yellow River basin in Henan Province that were formed before 1919 as the research object. Information on the ancient river was obtained through a literature search as well as via field research, and the important tributaries of the Yellow River in different periods were mapped using ArcGIS 10.0 software. The nearest neighbor index, kernel density estimation, standard deviation ellipse and other methods were adopted to analyze the spatiotemporal characteristics of the traditional villages. The factors that influenced the evolution of traditional villages were explored in depth by combining changes in the course of the river and water conservancy projects. The results show that the formation of traditional villages along the Yellow River in Henan Province has experienced a historical track of growth, contraction, growth and prosperity and stability. The traditional villages along the Yellow River in Henan Province generally show a clustered pattern, forming a dense concentration of traditional villages in the middle reaches of the Yellow River. The center of gravity shows a migration trend from southeast to northwest. In terms of influencing factors, the spatiotemporal evolution of the relationship between villages and their distance to water is closely related to climatic fluctuations, changes in channel, water conservancy projects and social and cultural factors. This paper deepens our understanding of the relationship between traditional village evolution and watersheds by improving the consistency between village spatial distribution and historical geography and provides a useful theoretical reference for the sustainable development of China's traditional villages. [ABSTRACT FROM AUTHOR]

Climate change has a profound impact on human survival and development. Climate change is an energy and economic issue, which should be driven by technology. Total factor energy efficiency (TFEE) improvement is undoubtedly a breakthrough in solving energy problems. In this paper, the fixed effect model, impulse response function, and threshold regression model are used to test the complex relationship between export sophistication and TFEE. The results reveal that export sophistication improvement leads to higher TFEE. The impulse response results verify the existence of a phased and positive correlation between export sophistication and TFEE. Finally, the relationship between export sophistication and TFEE significantly varied across different threshold levels of regional corruption score, economic development, and openness. This article led the foundation for supporting China's export sophistication promotion strategy and also provides a reference for energy efficiency improvement and energy crisis response in the post-pandemic era. [ABSTRACT FROM AUTHOR]

Recent years have witnessed climate change characterized by increasingly frequent extreme precipitation events, and the assumption of stationarity in traditional frequency analyses is gradually being questioned. In terms of the current research status in China, there is a lack of thorough investigations on the linkage between extreme precipitation and climate change. This paper aims to determine the dominant climate indices as well as the corresponding significant time scales and periods affecting extreme precipitation over China for dynamic assessments of the upcoming rainstorm risk. Correlations between 15 climate indices and precipitation extremes, as well as the correlations among climate indices, are fully explored to identify potential predictors for non-stationary modeling. Then, 21 non-stationary generalized extreme value (GEV) models are constructed, and the optimal covariates as well as their lag times with extreme precipitation at 769 stations are ascertained in a Bayesian framework. Finally, a complete predictive process is developed, and the national rainstorm risk under non-stationary conditions is assessed. The results indicate that precipitation extremes remain stationary only at 74 stations (less than 10%). WPI is dominant in modeling the variability in precipitation extremes for nearly 22% of the total stations, ranking first among all the climate indices. The predominant time scale affecting extreme precipitation at the majority of stations is 3 months. Ignoring the non-stationarity of extreme precipitation inevitably leads to misperceptions of rainstorm risks, and the spatial distribution of the maximum case of the design rainstorms under non-stationary conditions differs remarkably from that under stationary conditions. Our findings have important implications for the in-depth understanding of the real drivers of extreme precipitation non-stationary and enable advanced predictions of rainstorm risks for mitigating subsequent disasters. [ABSTRACT FROM AUTHOR]

The influences of human activity on regional climate over China have been widely reported and drawn great attention from both the scientific community and governments. This paper reviews the evidence of the anthropogenic influence on regional climate over China from the perspectives of surface air temperature (SAT), precipitation, droughts, and surface wind speed, based on studies published since 2018. The reviewed evidence indicates that human activities, including greenhouse gas and anthropogenic aerosol emissions, land use and cover change, urbanization, and anthropogenic heat release, have contributed to changes in the SAT trend and the likelihood of regional record-breaking extreme high/low temperature events over China. The anthropogenically forced SAT signal can be detected back to the 1870s in the southeastern Tibetan Plateau region. Although the anthropogenic signal of summer precipitation over China is detectable and anthropogenic forcing has contributed to an increased likelihood of regional record-breaking heavy/low precipitation events, the anthropogenic precipitation signal over China is relatively obscure. Moreover, human activities have also contributed to a decline in surface wind speed, weakening of monsoon precipitation, and an increase in the frequency of droughts and compound extreme climate/weather events over China in recent decades. This review can serve as a reference both for further understanding the causes of regional climate changes over China and for sound decision-making on regional climate mitigation and adaptation. Additionally, a few key or challenging scientific issues associated with the human influence on regional climate changes are discussed in the context of future research. [ABSTRACT FROM AUTHOR]

In response to global climate change, the Chinese Government has taken numerous measures to promote low-carbon management practices, but the overall adoption rate has been lower than expected. Empirical studies on the path dependence of farming experience, that is, long-standing planting concepts that will hinder farmers from adopting new technologies, have not been reported. Hence, to fill the research gaps, this paper uses survey data from 805 rice farmers in Zhejiang, Hubei, and Jiangxi provinces, China, to examine the impact of farming experience on the adoption of soil testing and fertilizer recommendations. The results show that farming experience significantly negatively affects the adoption of low-carbon practices, especially among farmers with low resource endowment. However, farmers, who make decisions based jointly on farming experience and social networks, are more likely to adopt low-carbon practices. This means that as long as farming experience is used reasonably, for example, by broadening the social network of farmers and urging them to form a decision-making method that comprehensively utilizes farming experience and social networks, it can also demonstrate value. Our findings contribute meaningfully to the development of efforts to promote the adoption of low-carbon management practices in China. [ABSTRACT FROM AUTHOR]

This paper used original survey data in Shandong province of China to depict local farmers' perceptions of combined climatic and market risks, and their adaptive behaviors. Two Logit models were developed to capture the empirical relationship between farmers' risk perception of the combined risks and adaptive behaviors. Results show that farmer's risk perception of climate change and market significantly affected their adoption behavior of adaptive measures, including perception of rainfall decrease, gale decrease, drought increase, and price fall of agricultural products. Moreover farmers' adaptive behaviors are more sensitive to their perceptions of extreme climatic events. Addition of demographic factors can improve the explanatory capacity of the Logit model. As illustrated by the models, male and better educated farmers have greater willingness to take actions for averting risks, while household heads with bigger farm are more reluctant. A majority of respondents chose to construct farm structure for irrigation and water conservation to avert climatic risks. Insurance, which is regarded as an effective tool to help farmers mitigate risks and avoid losses, has not been sufficiently accepted by farmers in rural areas in Shandong province. This underscores the need of effective communication of knowledge to enhance farmers' coping capacity and to encourage their active response to risk. [ABSTRACT FROM AUTHOR]

As the largest terrestrial organic carbon pool, soil organic carbon (SOC) plays an important role in the global carbon cycle, but it is easily affected by land use and climate change. The Huang-Huai-Hai Plain (HHHP) in China, which is dominated by dry farming, is experiencing rapid farmland loss and gain (farmland use transition), which may cause drastic changes in SOC storage. To clarify the effects of farmland use transition on SOC in dry farming areas, this study adopted a land use transition matrix and spatial analyst tool to explore the characteristics of farmland use transition and its effects on SOC content and stocks in the 200 cm soil layer in the HHHP from 2000 to 2020. The results showed that the farmland use transition in this area during that time period resulted in a net decrease of 16.06 × 103 km2 of farmland, of which urban built-up land and rural settlements led to a net reduction of farmland by 9.74 × 103 km2 and 6.43 × 103 km2, respectively, which was mainly affected by climate, topography, and economic factors. In addition, the farmland use transition resulted in a net increase in SOC storage of 6.13 Tg C, contributing 44.32% of the net increase. This is likely because the change in land use type led to a change in soil bulk density and soil gravel content while altering the SOC content, which in turn affected the SOC stock. This study has important implications for addressing global climate change and maintaining sustainable agricultural resources. [ABSTRACT FROM AUTHOR]

Technical change has a pivotal role to play in low-carbon development. Recent research has offered different insights regarding the effect of technical change on CO2 emissions but ignored the bias of technical changes which lead to changes in CO2 emissions. To fill the gap, this paper uses the 2008 to 2015 provincial-level data on China's 22 industrial sub-sectors to investigate both the effect of directed technical change on CO2 emissions and its heterogeneity. We find that the technical change in most industrial sectors in China was capital-biased, although a labor-biased trend was evident. Labor-biased technical change is conducive to CO2 reduction, while capital-biased technical change has the opposite effect. Moreover, this effect is different by developmental periods, industries, and regions. Therefore, we propose that the government promotes labor-biased technical change based on the differentiated characteristics. [ABSTRACT FROM AUTHOR]

Intensifying climate change significantly impacts residential electricity consumption, especially in developing countries, such as China, that are experiencing rapid income growth. By combining meteorological and monthly household consumption survey data, this study explores the response function of residential electricity consumption to temperature in China from a micro perspective. Future residential electricity demands and related CO2 emissions are then forecast under different climate scenarios. Overall, the response function is U-shaped, and one additional day above 34 °C will increase monthly residential electricity consumption by 2.11%. Global warming will more likely increase the electricity burden on low-income groups. There will be notable seasonal changes in electricity demand in the future, and the largest increase will occur in August. The total demand for residential electricity caused by temperature change will show a fluctuating growth trend, from 0.8% and 1% in 2025 to 2% and 2.9% in 2060 under the RCP4.5 scenario and RCP8.5 scenario, respectively; meanwhile, this demand will be accompanied by a cumulative increase in carbon dioxide emissions. [ABSTRACT FROM AUTHOR]

Ammonia (NH3) can interact with other trace chemicals in the atmosphere, significantly impacting atmospheric chemistry and global climate change. China is a largely agricultural country with high consumption of nitrogen fertilizer and large livestock herds, resulting in high NH3 emissions. In this study, a comprehensive county-level inventory of Chinese NH3 emissions from 2013 to 2018 was compiled. Based on previous research, an estimate of NH3 emissions from household coal combustion was added to the inventory. The estimation of emissions from open biomass burning was improved by using a method based on fire radiative energy (FRE). The total NH3 emissions in China increased from 2013 (9.64 Tg) to 2015 (9.75 Tg), and then decreased to 9.12 Tg in 2018. Emissions from fossil fuels reached a peak value in 2018, accounting for 8.4% of total emissions, while fertilizer application and livestock waste were responsible for fewer emissions than in previous years, accounting for 27.7% and 49.9% of the total from non-fossil fuel sources, respectively. The highest emission rates were in central and southwestern China. Seasonally, NH3 emissions peaked in spring and summer. The inventory had a 1-km spatial resolution and a monthly temporal resolution, which confirmed its suitability for global and regional air quality simulations. [ABSTRACT FROM AUTHOR]

In the context of climate change, the impact of extreme precipitation and its chain effects has intensified in the southeastern coastal region of China, posing a serious threat to the socioeconomic development in the region. This study took tropical cyclones–extreme precipitation–flash floods as an example to carry out a risk assessment of flash floods under climate change in the Yantanxi River Basin, southeastern China. To obtain the flash flood inundation characteristics through hydrologic–hydrodynamic modeling, the study combined representative concentration pathway (RCP) and shared socioeconomic pathway (SSP) scenarios to examine the change of flash flood risk and used the geographical detector to explore the driving factors behind the change. The results show that flash flood risk in the Yantanxi River Basin will significantly increase, and that socioeconomic factors and precipitation are the main driving forces. Under the RCP4.5-SSP2 and RCP8.5-SSP5 scenarios, the risk of flash floods is expected to increase by 88.79% and 95.57%, respectively. The main drivers in the case study area are GDP density (q = 0.85), process rainfall (q = 0.74), asset density (q = 0.68), and population density (q = 0.67). The study highlights the influence of socioeconomic factors on the change of flash flood disaster risk in small river basins. Our findings also provide a reference for regional planning and construction of flood control facilities in flash flood-prone areas, which may help to reduce the risk of flash floods. [ABSTRACT FROM AUTHOR]

This study examined the effects of climate change on rice production in 30 Chinese provinces spanning 1998–2017. The study used the pooled mean group technique to capture the long-run and short-run effects of changing climatic conditions on rice production. It further employed the Dumitrescu–Hurlin panel causality test to examine the path of causality between the key variables and rice production. The study found that, in the long run, average temperature negatively influenced rice production, but average rainfall had a positive effect on rice production. The results indicated that the cultivated area and fertilizer usage were positively related to rice production in the long run. The short-run results accentuated that average temperature favourably influenced nationwide rice production, whereas average rainfall had no substantial effect on national rice production. The cultivated area had a significant positive short-term relationship with rice production, although the impact of fertilizer usage on rice production was negligible in the short run. Besides, the results established a bidirectional causality between rice output and the cultivated area, but there was a one-way causality running from fertilizer usage to rice output. Finally, the results indicated that, except for rainfall, a unidirectional causality exists between temperature and rice production. The study, therefore, recommends that in the case of crop failure due to weather conditions, policymakers could implement a new pricing policy to mitigate the deterioration of the farmers' income. The government must also develop and implement an insurance scheme that compensates farmers for catastrophes induced by rainfall deficiency. [ABSTRACT FROM AUTHOR]

Understanding future drought risks plays a crucial role in developing climate change adaptation strategies and in enhancing disaster resilience. However, previous studies may lead to biased conclusions due to the neglect of two factors, including the relative performance of climate simulations and the uncertainty in drought characterization. In this study, Bayesian model averaging is used to merge five regional climate model simulations and to project future changes in hydroclimatic regimes over China under two representative emission scenarios (RCP4.5 and RCP8.5). Drought characteristics, including drought severity and duration, are extracted using the Standardized Precipitation Evapotranspiration Index (SPEI). A Bayesian copula approach is used to uncover underlying interactions of drought characteristics and associated uncertainties across 10 climate divisions of China. The regional return periods of drought characteristics are used to assess future changes in multidimensional drought risks and the probability of extreme droughts. Our findings reveal that the variations in drought characteristics are generally underestimated by the ensemble mean (AEM) simulation. The Bayesian framework improves the reliability and accuracy of hydroclimate simulations and better reproduces the drought regimes compared to the AEM simulation. The drought duration and severity are projected to substantially increase for most areas of China based on the Bayesian framework, but the AEM simulation may lead to multiple opposite behaviors, especially under RCP4.5. The estimated joint risk from drought duration and drought severity is expected to increase under both emission scenarios. The likelihood of extreme droughts is also projected to increase as the radiative forcing increases. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)