Shaanxi Province Of China Research Articles

Analysis and Forecast of Land Use Based on Patch-generating Land Usage Simulation (PLUS) Model: A Case of Shaanxi Province in China

China has witnessed a substantial number of land-use type shifts and broken land-use distribution patterns as a result of massive irrational exploitation, which has led to problems like a significant loss of environmental habitability and declining ecosystem functioning. Investigating the origins and mechanisms of land-use change is essential. The results show that earlier land-use models, which also lacked simulation studies and projections of land-use changes in Shaanxi Province over a 20-year timeframe, find it difficult to integrate quantitative and spatial pattern forecasts. This paper uses the patch-generating land usage simulation (PLUS) model to assess Shaanxi Province's land use patterns between 2000 and 2020 and predict land usage for the year 2025.Farmland, woodlands, and grassland are the three main land types that make up most of the Shaanxi Province's total area. Forests make up most of the land that will be transferred between 2000 and 2020, while agriculture makes up most of the land that will be transferred. Most of the elements influencing the spread of all land use types were rainfall and Normalized Difference Vegetation Index (NDVI). The central and southern regions of Shaanxi province will primarily experience changes in land use by 2025.This research offering a resource for Shaanxi Province's science of land use structure and efficient land resource allocation.

Time's ticking! Effects of deadline on the utilization of health services: Evidence from a cluster-randomized controlled trial

The low utilization of health services in rural areas is an essential factor affecting the health level of rural residents. Based on insights from behavioral economics, this study evaluates the average treatment effect of imposing a deadline on vision-care vouchers (i.e., the voucher-deadline intervention) on the utilization of health services. Using the vision health as a case study, a non-masked cluster-randomized controlled trial was conducted in 13 rural schools of a county in Shaanxi Province of China during 2018–2019. Among 1270 baseline sample students with myopia, 610 students from 6 schools were randomly assigned to receive vouchers with no deadline (i.e., the Control group), and 660 students from 7 schools to receive vouchers with a 30-day deadline (i.e., the Deadline group). The results showed that the voucher-deadline intervention significantly improved the vision-center visiting rate by 13.2 percentage points within 30 days and shortened the interval between visits (i.e., increased timeliness) by 18.7 days. In comparison, the vision-center visiting rate and the interval between visits among the control group were 16.7% and 30.5 days, respectively. However, considering a longer time frame (6 months), we found that intervention only shifted students' utilization to before the voucher expires without improving overall utilization rate. The loss aversion and time pressure induced by the voucher deadline are possible explanations for the treatment effects. This study suggests insights from behavioral economics can enhance the design of public health programs to improve the utilization and the cost-effectiveness of health-service programs. Trial registrationisrctn.org Identifier ISRCTN03252665.

Forest Carbon Storage and Carbon Sequestration Potential in Shaanxi Province, China

The carbon storage of forest vegetation plays a crucial role in the terrestrial carbon budget. The objective of this study is to elucidate the current biomass carbon storage and sequestration capacity, as well as the future carbon sequestration potential of forest ecosystems in Shaanxi Province of China, thus providing data support and policy references for sustainable forest management and the response of carbon sequestration to climate change. Based on the data obtained from the seventh and ninth forest resource inventories, the regional biomass conversion factors, and carbon measurement parameters, the biomass conversion factor method is employed to estimate the biomass storage and carbon sequestration capacity of forest ecosystems. (1) The total carbon storage of forest lands in Shaanxi Province was 285.20 Tg. The carbon storage of arbor forests, sparse woodlands, scattered forests, four-side trees, shrub woodland, and bamboo forests were 237.09 Tg, 2.93 Tg, 12.30 Tg, 5.98 Tg, 26.35 Tg, and 0.56 Tg, respectively. (2) Over the 10 years from the seventh (2005) to the ninth (2015) forest resource inventories, the carbon storage of forests increased from 207 Tg to 285 Tg, with a total increase of 78.01 Tg (37.65%), demonstrating a significant carbon sink function. (3) From 2015 to 2060, the carbon density of arbor forests will increase from 33.53 Mg/ha to 46.90 Mg/ha, and the carbon storage will increase from 237 Tg to 432 Tg. These results indicate that forests have significant net carbon sequestration capacity and can play an important role in achieving China’s carbon peak and carbon neutrality goals. Aiming for carbon neutrality, improving forest management, along with protecting and utilizing forest resources through technological innovation, will become the driving force for increasing carbon storage in Shaanxi Province in the future.

A Hermeneutic Phenomenological Inquiry: Risk and Protective Factors for the Left-Behind Children in Shaanxi Province, China, With Gender Comparison

ABSTRACT This research presents the lived experience of left-behind children living in Shaanxi province of China through the lens of resilience theory. A hermeneutic phenomenological inquiry is employed to identify inner and outer risk factors faced by left-behind children at the primary school level and explore their protective factors (internal assets and external resources). This study attempts to determine how these protective factors can moderate or reduce the repercussions of risk exposure on the children’s development. Unstructured observations, including on-site observations of the classroom, life, and interpersonal relationships, were conducted in 2018 and remote classroom observations were completed in 2020 due to the pandemic. There were 51 students (including 28 left-behind children) observed in total. Fourteen 9- to 13-year-old left-behind children, eight guardians, and six teachers participated in interviews. Remote pre-interview activities were specifically employed in 2020. The findings indicate this group’s internalized and externalized problems, and the internal assets and external resources. This study would potentially identify viable targets for interventions and socially inclusive education. Resilience-based interventions in policies and practices are recommended to intervene in the trajectory from risk exposure to associated negative effects.

Investigation of Chinese Preschool Teachers’ Attitudes and Beliefs toward Science Teaching

The science attitudes and beliefs of preschool teachers in Shaanxi province of China were explored. 185 preschool teachers participated in the survey. The findings revealed that the preschool teachers had the positive attitudes and beliefs toward science teaching. However, there are clearer differences between preschool teachers’ science attitudes and beliefs and practical teaching. They may escape teaching science activities in teaching when they meet difficulties in the insufficient teaching time, inadequate knowledge and auxiliary materials. In addition, the factors influencing teachers’ science attitudes and beliefs were studied. The gender and education level of preschool teachers had no significant effect on their scientific attitudes and beliefs. It is worthy note that the preschool teachers with bachelor’s degree were more willing to carry out some science activities than the preschool teachers with university (3 years). Although there is no significant difference in the attitudes and beliefs of the preschool teachers with different teaching experiences, teaching experiences had a significant difference in the challenge dimension. The preschool teachers with less than 2 years teaching experiences are more confidence in science teaching. Based on the findings, some suggestions are supplied: improving education level, building specific and clear standards, providing more related auxiliary material supports.

First Record of Meloidogyne incognita on Maize (Zea mays) in Shaanxi Province of China.

Meloidogyne incognita can severely infect and harm some crops in temperate zones under open field in some cases, even though it's more widespread and economically important in tropical and subtropical regions (Eisenback, 2020). In early June 2022, patches with poor growth maize plants were observed in Dali County (109.93E, 34.80N) of Shaanxi province, China. The infected maize plants were stunted with galled and small roots. Females, males, second-stage juveniles (J2s) and egg masses were extracted and collected from galled roots and soil for morphological identification. The perineal pattern of females had a dorsally high square arch lacking obvious lateral lines. Stylet knobs of females were rounded and set off. The excretory pores were at level of or posterior to stylet knobs, 10-20 annules behind head. The head cap of males was flat to centrally concave, the stylet shaft constricted slightly at the junction with the knobs, and stylet knobs were broadly elongate to round, set off, flat and the width usually greater than the height. Measurements of females (n=20) were: body length (L)= 734.63 ± 79.24 µm (642.15 µm to 788.48 µm); maximum body width (W)= 487.14 ± 50.79 µm (426.09 µm to 556.42 µm); stylet length (ST)= 14.78 ± 1.57 µm (13.17 µm to 16.56 µm); and distance from dorsal esophageal gland opening to the stylet knobs (DGO)= 3.55 ± 0.13 µm (3.17 µm to 3.90 µm). Measurements of males (n=10) were: L=1483.76 ± 134.81 µm (1174.39 µm to 1635.62 µm); W=44.37 ± 3.28 µm (39.76 µm to 50.26 µm); ST= 19.76 ± 1.05 µm (17.84 µm to 22.36 µm); and DGO= 3.48 ± 0.28 µm (3.08 µm to 3.87 µm). The morphological characteristics of this nematode were consistent with Meloidogyne incognita (Kofoid and White, 1919) Chitwood, 1949 (Williams, 1973; Eisenback and Hirschmann, 1981). Moreover, the identification was further confirmed by PCR using two pairs of primers, D2A/D3B and NAD5F/R, with DNA extracted from 20 individual females, respectively (Subbotin et al., 2006; Janssen et al., 2016). Both the D2-D3 region sequence (MZ665547) amplified by D2A/D3B and the 597 bp sequence (MZ665548) amplified by NAD5F/R showed >99% identity with sequences of other M. incognita isolates. Both morphological and molecular data identified the root-knot nematodes on maize as M. incognita. Then ten maize seedlings maintained in pots containing autoclaved sandy soil at 25°C were each inoculated with 2000 freshly hatched J2s of the original population of M. incognita. At 45 days after inoculation, all inoculated plants developed gall symptoms on the roots similar to those in the field. And five non-inoculated maize seedlings showed no symptoms. Females dissected from inoculated plants were identified to be M. incognita with species-specific primers IncK-14F/IncK-14R (Randig et al., 2002). According to consultation, in the same field root-knot nematode infected carrots were harvested in November last year, the field was left unploughed until March when maize was sowed. As Dali County locates in north temperate zone with a warm temperate climate, where the average annual temperature is 14.4°C, and the highest and lowest temperature was 18°C and -9°C in last winter, the overwintering rate of M. incognita in open field in such area needs further study.

Long-term energy-environment-economic programming under carbon neutrality target: a study on China’s regional energy transition pathways and CO2 mitigation strategies

ABSTRACT Carbon neutrality is one of the key issues in mitigating global climate change. The carbon neutrality process accelerates the global energy transformation from fossil energy to renewable energy. Among the main indicators related to carbon neutrality proposed by the Chinese government, those directly related to energy include carbon emission intensity, the proportion of non-fossil energy consumption, and the total installed capacity of wind and solar power generation. It can be seen that the transformation of energy development is crucial for realizing carbon neutrality. Meanwhile, the correlation between carbon neutrality and economic development is not antagonistic. On the contrary, carbon neutrality can not only enhance the quality and efficiency of traditional industries but also improve the overall development of society. Therefore, the coordinated and sustainable developments of the energy - economy- environment (3E) system is the priority for achieving the target of carbon neutrality. However, the impact of carbon neutrality on regional development requires long-term consideration, which increases the difficulties of comprehensive planning. In this study, a long-term comprehensive programming model of regional energy and economic development under carbon peaking and carbon neutrality targets was established. A carbon neutrality assessment indicator (CNAI) framework was constructed to assess the effect of carbon neutrality actions in different regions. Meanwhile, a LEAP model was developed to simulate energy demand and carbon emissions. The multi-target weights determined by assessment results of CNAI and the parameters range set by LEAP model were the basis of the multi-objective optimization model to support regional development. Furthermore, the proposed method was applied to Shaanxi province in China, which offered quantitative targets and suggestions for energy structure transformation and industrial structure adjustment. The results show that coal and oil will account for 6.25% of the total energy consumption and the proportion of secondary industry will drop to 25.40% by 2060. The method established in this study provides a new scientific approach for resolving multi-objective trade-offs in long-term programming and determining a reasonable optimization range for regional development under climate mitigation policies.

Online attention dynamics: The triangle framework of theory, big data and simulations

In computational social science era, the theory, big data and simulations should be well combined and deeply integrated, and this work serves as a typical example. Online collective actions have great impacts to societies, and we focus on the life cycle pattern of coexisting multiple online cases. We propose the analysis framework of “Theory-Big Data-simulation” (TDS). The theory includes two essential action rules of netizens. We combined macro-level analysis (big data analysis) and micro-level behavior (agent-based modeling), to surpport the theory. For the big data, macro-level life cycle pattern (persistent) is caused by micro-level individual actions. For macro-level verification, basic assumption is that netizens are allocating limited attentions to limited coexisting online events, which has been supported by our big data from the Weibo platform (N=197,985). For the micro-level, we use agent-based modeling to verify the behavioral rules of Netizens. We set two agent categories (Netizens & Hots), which behave and interact autonomously. Based on multiple-round simulations, we obtained optimal solution outcomes. Besides, we have implemented mutual validation between big data and agent-based modeling, which has not been considered before. Three big data sets have been applied to check both validity and robustness of our solutions. For the dataset A, we use 89 online cases of the Shaanxi Province in China. For the dataset B, we used 147 online cases of the whole China in 2021. For the dataset C, we used 138 online cases under specific topic (the pandemic). It suggests that three optimal solutions have both validity and robustness. As three big data sets have different levels, cases, topics and durations, the model’s generality can be well supported. Hence, general behavior pattern of the Netizens can be revealed, which is of great significance for investigating human behavior online. Moreover, our work contains rich managerial implications, which helps to improve social simulation and prediction. Also, our TDS analysis framework can support or inspire other researches.

Impact of rural capital outflow on sustainable economic growth: evidence from Shaanxi Province of China

The capital constraint and underinvestment faced by rural residents are the pivotal factors limiting economic growth. The rural capital outflow further magnifies the income gap between urban and rural residents, seriously undermining rural areas’ capital accumulation and sustainable development. For this purpose, we perform a theoretical and empirical investigation of the impact of rural capital outflow on economic growth. The results reveal that rural capital outflow occupies investment opportunities for rural economic development that adversely affect economic growth. Secondly, rural economic growth has an inertial effect and generates path dependence on capital outflow. The calculation of rural capital outflow in northern Shaanxi, Guanzhong, and southern Shaanxi regions of China reveals that capital outflow in northern Shaanxi is insignificant and shows signs of capital return, while southern Shaanxi and Guanzhong emerge as key regions for capital outflow.Policymakers must adhere to long-term initiatives to reform and refine multi-level capital markets that support agricultural development in rural areas and build a sound rural financial market order for sustainable economic growth.

Inferring a Causal Relationship between Environmental Factors and Respiratory Infections Using Convergent Cross-Mapping.

The incidence of respiratory infections in the population is related to many factors, among which environmental factors such as air quality, temperature, and humidity have attracted much attention. In particular, air pollution has caused widespread discomfort and concern in developing countries. Although the correlation between respiratory infections and air pollution is well known, establishing causality between them remains elusive. In this study, by conducting theoretical analysis, we updated the procedure of performing the extended convergent cross-mapping (CCM, a method of causal inference) to infer the causality between periodic variables. Consistently, we validated this new procedure on the synthetic data generated by a mathematical model. For real data in Shaanxi province of China in the period of 1 January 2010 to 15 November 2016, we first confirmed that the refined method is applicable by investigating the periodicity of influenza-like illness cases, an air quality index, temperature, and humidity through wavelet analysis. We next illustrated that air quality (quantified by AQI), temperature, and humidity affect the daily influenza-like illness cases, and, in particular, the respiratory infection cases increased progressively with increased AQI with a time delay of 11 days.

Agricultural irrigation water price apportionment and sharing

Abstract Studying the apportionment and sharing of agricultural water prices is necessary for clarifying the relationship between agricultural water price apportionment and government responsibility and ensuring the benign operation of irrigated areas. From the perspective of the versatility of irrigation water, irrigation benefits are classified as internal or external; the degree of benefit is measured using the proportion of irrigation water per cubic metre of internal and external benefits. This is used as an allocation coefficient to reasonably share prices based on farmers’ water price tolerance, build agricultural water price apportionment and sharing models, and calculate the range of water prices borne by farmers and the government in the Baojixia Irrigation District in the Shaanxi Province of China. Results showed that the apportionment coefficients of farmers and government were 0.85 and 0.15, respectively. For grain crops, the farmers' price range was 0.115–0.508 yuan/m3, while that of the government was 0.516–0.909 yuan/m3; for cash crops, these values were 0.566–3.009 and 0.154–0.458 yuan/m3, respectively. The results of this study support the formulation of agricultural water prices and provide a theoretical reference for reducing the water burden of farmers and promoting the high-quality development of the regional economy

Health Literacy Among University Students in Shaanxi Province of China: A Cross-Sectional Study.

An adequate level of health literacy will help university students to better respond to public health emergencies and reduce unintended harm caused by public health events. The objective of this study was to assess the health literacy levels of students from Universities of Shaanxi province of China, in order to provide a basis for the development of health literacy promotion plan for university students. An online cross-sectional questionnaire survey was conducted at five universities in Shaanxi Province of China on the Wen-Juan-Xing online platform. A purposive sampling method was used to 1578 students via self-administered questionnaire. Comparisons of means were made using the t-test and ANOVA, and comparisons of ratios or composition ratios were made using the χ2 test. The mean score for health literacy was (105.33±10.14) out of 135, and the mean scores for the three dimensions of health knowledge, attitudes and practices were (36.093±4.192), (34.178±4.227) and (35.059±4.515) respectively. Of the total sample, 39.2% were classified as sufficient in health literacy. Female students had higher health literacy level than male students (t=4.064, p=0.044), lower grade students scored higher than higher grade (F=3.194, p=0.013), students from urban cities scored higher than those who came from rural areas (t=16.376, p<0.001), and university students with health education experience scored higher than those without (t=24.389, p<0.001). University students' health literacy is closely related to their gender, grades, family location and health education experience.

A Comprehensive Correction Method for Radiation Distortion of Multi-Strip Airborne Hyperspectral Images

Airborne hyperspectral imaging plays an increasingly important role in environmental monitoring. However, due to the limitations of the acquisition conditions, there are uneven radiation and chromatic aberrations in the mosaic data. Accurate preprocessing of the original data is the premise of qualitative and quantitative remote sensing. In this study, we proposed a comprehensive radiation distortion correction method that integrates radiation attenuation difference correction, topographic correction, and multi-strip images consistency adjustment (RA-TOC-CA). First, the radiation attenuation equation was constructed by combining the viewing geometry, terrain, and the elevation difference between the UAV and the ground to eliminate the radiation attenuation difference of pixels acquired at the different instantaneous field of view (IFOV). Second, an improved kernel-driven BRDF model was built combining terrain information and illumination-viewing (flight attitude and sensor IFOV) geometry to eliminate the radiation unevenness and BRDF distortion caused by topography. Third, adjusting the reflectance of multi-strip images according to the homonymous points’ reflectance of adjacent strips should be equal, eliminating the radiation differences between multiple strips. Based on multi-strip airborne hyperspectral images collected in the Shaanxi province of China, the correction results of the RA-TOC-CA method were compared with those of the SCS+C and Minnaert+SCS methods regarding various evaluation criteria. The results showed that SCS+C and Minnaert+SCS can reduce the topographic effect but cannot eliminate the reflectance difference at the edges of adjacent images, and SCS+C overcorrects the reflectance. RA-TOC-CA weakened the topographic effects and brightness gradient, which was physically stable and generalizable. Compared with previous studies, RA-TOC-CA provided a complete radiation distortion correction method for airborne hyperspectral images and had a solid theoretical basis. This study introduces an effective method for radiation distortion correction of airborne hyperspectral images and provides technical support for large-scale applications of hyperspectral images.

Comprehensive impact assessment of carbon neutral pathways and air pollution control policies in Shaanxi Province of China

Low-carbon policies will have a significant impact on air quality. This study builds an integrated assessment model to systematically assess the impacts of different carbon neutral pathways and air pollution control policies in Shaanxi province. We found that the contribution rate of low-carbon policies to air quality improvement will be greater than end-of-pipe control. Carbon neutral pathways could prevent 5 to 10 thousand premature deaths and bring 5.7 to 10.4 billion USD in environmental health benefits. From the perspective of cost-benefit, low-carbon policies can bring positive benefits of 0.27% of GDP before 2030. Carbon reduction costs will increase rapidly after 2035, and the loss of total social-economic benefits in 2060 is 3.16% to 16.57%. Achieving carbon neutrality means a disruptive technological shift, reducing the policy costs of climate governance by aggressively developing more zero-carbon and negative-carbon technologies.

Virulence Phenotyping and Molecular Genotyping Reveal High Diversity Within and Strong Gene Flow Between the Puccinia striiformis f. sp. tritici Populations Collected from Barberry and Wheat in Shaanxi Province of China.

Emergence of new Puccinia striiformis f. sp. tritici races that overcome resistance of wheat cultivars is a challenging issue for wheat production. Although sexual reproduction of the fungus on barberry plants under field conditions in the spring in China has been reported, the diversity of the pathogen on barberry plants and the relationship to the population in wheat fields have not been determined. In the present study, two P. striiformis f. sp. tritici populations collected in western Shaanxi Province in May 2016, one from barberry plants (103 isolates) and the other from nearby wheat crops (107 isolates), were phenotyped for virulence and genotyped with simple sequence repeat (SSR) markers. The phenotypic and genotypic data of the two populations were compared to determine their relationships. A total of 120 races, including 29 previously known races (seven were shared by the two populations) and 91 new races (35 from barberry and 56 from wheat), were identified. Similarly, a total of 132 multilocus genotypes, including 51 only from barberry, 77 only from wheat, and four from both, were detected using the SSR markers. Analyses of molecular variance identified high (93%) genetic variance within populations and low but still significant variance (7%) between the populations. Nonparametric multivariate discriminant analysis of principal components and STRUCTURE analysis showed that the two populations had a close relationship with little genetic differentiation (FST = 0.038) and strong gene flow (Nm = 6.34, P = 0.001) between them. Although the analysis of linkage disequilibrium indicated clonal populations, the isolation of P. striiformis f. sp. tritici from barberry plants and the high genetic diversities in the barberry and wheat populations suggest that barberry plants provide aeciospores to infect wheat crops in the area. The information is useful for understanding stripe rust epidemiology and management of the disease.

Forecasting CO2 emissions using a novel fractional discrete grey Bernoulli model: A case of Shaanxi in China

To accurately predict CO2 emissions, this paper proposes a novel fractional accumulation method and constructs a novel fractional discrete grey Bernoulli model DGFGBM (1,1,tα). The manta ray foraging optimization (MRFO) algorithm is used to find the parameters of the new model, and the carbon emission data of Shaanxi Province in China and three of its cities are applied to verify the effectiveness of the new model. The CO2 emissions of the four regions from 2020 to 2024 are predicted, and the following conclusions can be drawn. The new fractional accumulation method is effective and reasonable and can make full use of new information and improve the prediction accuracy of the model. Compared with three other optimization algorithms, MRFO has more advantages in finding the optimal parameters of the model. Compared with other grey models and the ARIMA model, the DGFGBM (1,1,tα) model has better prediction performance. The prediction results show that from 2020 to 2024, the CO2 emissions of Shaanxi Province are expected to increase by 94.6278 million tons, an increase of 31.8%, and the CO2 emissions of Xi'an, Xianyang and Baoji are expected to increase by 22.4901 million tons, 16.3129 million tons and 7.3271 million tons, respectively, with growth rates of 45.81%, 46.51% and 34.07%, respectively.

Study on the strontium isotope standard in pig teeth excavated from Zaoshugounao site, Shaanxi Province of China

AbstractIn archaeology, strontium isotope analysis is developing into an efficient scientific technique for tracing the movement of prehistoric humans and animals. Determining the local bioavailable 87Sr/86Sr ratio range is the key to distinguishing whether the human or animal is indigenous to the local area. It has been shown that the 87Sr/86Sr ratio in the enamel of pigs can be an excellent sample to determine the range of local strontium isotope ratio at the site. However, pigs may not be all local at the site, and there is no special study on whether pigs’ different ages and tooth types impact the local strontium isotope characteristics. In this paper, the tooth enamel of 19 pigs from the Zaoshugounao site for strontium isotope ratio (87Sr/86Sr) by multicollector–inductively coupled plasma mass spectrometry. There were no significant differences in 87Sr/86Sr ratios of local pigs at different months and different molar types, which did not affect the determination of the local strontium isotope standard. Based on the local strontium isotope ratio range (0.711056–0.711476), we found that five pigs were non‐local. The Jing and Wei river basins (in Guanzhong basin) and northern Shaanxi are likely sources of non‐pigs.

Research on Resilience Evaluation of Coal Industrial Chain and Supply Chain Based on Interval Type-2F-PT-TOPSIS

As unexpected events such as natural disasters, the COVID-19 pandemic, and overseas containment have caused inevitable shocks to the energy industrial chain and supply chain, the current global energy crisis is intensifying, and different countries and regions have adopted different strategies according to the characteristics of their own national resource endowments in order to cope with energy security. Maintaining the security of the coal industrial chain and supply chain is a prerequisite for energy security to be effectively ensured, considering the main position of coal in China’s energy. Therefore, in the face of multiple uncertain risk factors under today’s momentous changes, this paper constructs an industrial coal chain and supply chain resilience evaluation indicator system from the perspective of resilience, based on four representational capabilities of resilience, namely preparedness, absorptive capacity, recovery capacity, and adaptability, in order to profoundly understand and enhance the resilience of the coal industrial chain and supply chain. An integrated method combining Interval Type-2 Fuzzy Prospect Theory and Technique for Order Preference by Similarity to an Ideal Solution (Interval Type-2F-PT-TOPSIS) is proposed for evaluating the resilience level of the coal industrial chain and supply chain. In the case of Shaanxi Province in China, it was found that the worst level of resilience of the coal industrial chain and supply chain in Shaanxi Province was in 2018, and the best was in 2021. Finally, based on the evaluation results, recommendations are provided to the key nodes of the industrial chain and supply chain in Shaanxi Province with a view to improving their resilience levels to cope with uncertain risks.

Life Cycle Assessment of Carbon Footprint of Green Tea Produced by Smallholder Farmers in Shaanxi Province of China

China is a major producer of green tea, and most of its green tea production comes from small farmers. Accessing the carbon emission status of this group can provide data support and a decision-making basis for the realization of carbon neutrality in China’s tea industry. In this study, the life cycle assessment method was used to analyze the carbon footprint of green tea produced by smallholder farmers in Liugou Village, Hanzhong City, Shaanxi Province. The results showed that the carbon emission intensity of green tea for its entire life cycle was 32.90 kg CO2eq kg−1 dry tea, and the carbon emission intensities of its consumption, processing, and cultivation were 14.90, 7.94, and 6.97 kg CO2eq kg−1, respectively. In the processing stage, emissions during steaming and drying accounted for 57%. The use of coal, complicated processing procedures, and older equipment were the main reasons for the high emissions in the processing stage. In the cultivation process, emissions mainly came from fertilizer production and its application in the field. The energy consumption of boiling water resulted in high carbon emissions in the consumption stage. This study suggests that building a scientific fertilization system for tea gardens, optimizing processing equipment and energy utilization structure, and cultivating the concept of low-carbon consumption will be the keys to promoting smallholder farmers to reduce carbon emissions. This study further emphasizes that we should focus on carbon emissions caused by the production processes of small farmers.

Factors Influencing Water Use Efficiency in Agriculture: A Case Study of Shaanxi, China

Enhancing agricultural water use efficiency can support Shaanxi Province’s transition to a sustainable agricultural economy. Using the data envelopment approach (DEA), we evaluated the water use efficiency in agriculture of various cities in the Shaanxi Province of China. Moran’s I and Dagum Gini coefficients were used to reveal spatial and temporal characteristics of agricultural water use efficiency. Furthermore, the geographically and temporally weighted regression (GTWR) model was used to investigate the effects of the agricultural economy, level of water-saving irrigation, cropping structure, environmental factors, and water supply structure on agricultural water use efficiency. The findings demonstrate that the average agricultural water use efficiency from 2011 to 2020 is low (0.796) in Shaanxi Province. Spatially there is considerable variation in water use efficiency between cities, with some highly efficient and some with very low efficiency. There is a strong negative spatial correlation between cities, with high efficiency cities tending to be adjacent to low efficiency cities. Moreover, the spatial differences in agricultural water use efficiency have increased over time, with the most significant increase within the northern region and between the north and central areas. Each influencing factor has a different impact from year to year and from city to city. Overall, the level of water-saving, the level of agricultural economics, the structure of the water supply, and environmental factors primarily have a negative influence. In contrast, the planting structure primarily has a favorable function. Therefore, to support the coordinated growth of agricultural water use efficiency, Shaanxi Province should improve its city-to-city cooperation and exchange of water use technology and experience, as well as develop differentiated water use measures appropriately for the development of each city according to local conditions.