WRKY transcription factors (TFs) constitute one of the largest protein families in higher plants, and its members contain one or two conserved WRKY domains, about 60 amino acid residues with the WRKYGQK sequence followed by a C2H2 or C2HC zinc finger motif. WRKY proteins play significant roles in plant development, and in responses to biotic and abiotic stresses. Pear (Pyrus bretschneideri) is one of the most important fruit crops in the world and is frequently threatened by abiotic stress, such as drought, affecting growth, development and productivity. Although the pear genome sequence has been released, little is known about the WRKY TFs in pear, especially in respond to drought stress at the genome-wide level. We identified a total of 103 WRKY TFs in the pear genome. Based on the structural features of WRKY proteins and topology of the phylogenetic tree, the pear WRKY (PbWRKY) family was classified into seven groups (Groups 1, 2a–e, and 3). The microsyteny analysis indicated that 33 (32 %) PbWRKY genes were tandemly duplicated and 57 genes (55.3 %) were segmentally duplicated. RNA-seq experiment data and quantitative real-time reverse transcription PCR revealed that PbWRKY genes in different groups were induced by drought stress, and Group 2a and 3 were mainly involved in the biological pathways in response to drought stress. Furthermore, adaptive evolution analysis detected a significant positive selection for Pbr001425 in Group 3, and its expression pattern differed from that of other members in this group. The present study provides a solid foundation for further functional dissection and molecular evolution of WRKY TFs in pear, especially for improving the water-deficient resistance of pear through manipulation of the PbWRKYs.

Land use/cover change is the second major contributor to carbon emissions, following energy emissions. Studying provincial land-use carbon emissions is crucial for achieving the “double carbon” goal. This study selects 16 prefecture-level cities in Shandong Province as the research object. It analyzes the spatial and temporal distribution pattern of carbon emissions in Shandong Province based on land-use data and energy consumption. In terms of net carbon emissions, this study utilizes the standard deviation ellipse and kernel density estimation to analyze net carbon emissions change from the municipal and regional perspectives. In terms of carbon ecological carrying capacity, not only the carbon ecological carrying capacity of forest and grassland was considered, but also the carbon ecological carrying capacity of crops in Shandong Province, which is a large grain province. Using the geographic detector to explore the drivers. Research findings indicate that carbon sources and sinks show a clear spatial and temporal distribution pattern, with the center of gravity of net carbon emissions extending to the northeast. Areas with high carbon ecological carrying capacity have high forest coverage, grassland coverage, and crop yields. Regarding driving factors, the urbanization rate, economic aggregate, and technological progress demonstrate significant explanatory power through single and interaction tests, suggesting that these factors are critical drivers of land-use carbon emissions within Shandong Province. Based on the spatiotemporal pattern analysis of land-use carbon emissions in Shandong Province, each city's growth rate and spatial distribution characteristics can be clarified, providing a scientific basis for the local government to formulate regional and differentiated emission-reduction policies. In addition, by exploring the driving factors of land-use carbon emissions in Shandong Province, the influence level of factors on carbon emissions can be revealed to provide references for formulating regional sustainable development strategies.

Land use/cover change has an important impact on global climate change and carbon cycle, and it has become another major source of carbon emission after energy consumption. Therefore, this study focuses on the main line of “land use carbon emissions-spatial and temporal patterns-influencing factors,” and selects 13 cities in Jiangsu Province as the research object. Based on the data of land use and energy consumption, combined with the method of land use carbon emissions and ArcGIS technology, this study conducted a quantitative analysis of the spatio-temporal distribution of carbon emissions in Jiangsu Province. The factors affecting the spatial distribution of carbon emissions from land use in Jiangsu Province were discussed by using Geographic detector. The results show that: 1) Carbon emissions in Jiangsu Province showed an overall growth trend, from 16215.44 ×104tC in 2010–23597.68 ×104tC in 2020, with an average annual growth rate of 4.55%, of which the construction land and watersheds had a greater impact on carbon sources and sinks, respectively. 2) During the period, there were significant differences in carbon emission levels among different cities in Jiangsu Province, and the land use carbon emission in Jiangsu Province showed a stable spatial pattern of “northwest—southeast.” The southern part of Jiangsu is always the hot area of carbon emission, while the cold spot area is mainly distributed in the northern and central parts of Jiangsu. 3) The interaction of factors such as economic development, industrial structure, energy intensity, land use and human activities is an important reason for the spatio-temporal differences of land use carbon emissions in Jiangsu Province. Among them, the level of urbanization, population size and economic aggregate have significant effects on carbon emissions.

With the economic development and rising living standards in rural China, the amount of household waste generated continues to increase, causing serious pollution to the environment and risks to public health. Promoting the classification of rural household waste is a critical way to improve the dwelling environment and control disease transmission in rural areas. Using the 2021 China Land Economic Survey (CLES) conducted in rural areas of Jiangsu province, China, this research explores how economic incentives and reputational incentives impact rural residents’ participation in household waste classification intention and behavior. The results show that most surveyed rural residents have the intention to classify their household waste, but only half of them perform the waste classification behavior. Furthermore, both economic incentives and reputation incentives have significant positive effects on rural residents’ intention and behavior regarding household waste classification, and there exists a complementary effect between them, which indicates that a combination of economic incentives and reputation incentives will increase the participation rate of rural residents in household waste classification. Finally, based on the findings, we put forward recommendations for rural waste management policies, including synergizing both economic incentives and reputation incentives, improving the mobilization system, and strengthening publicity and education on household waste classification.

In the context of achieving the goal of carbon neutrality, exploring the changes in land demand and ecological carbon stocks under future scenarios at the urban level is important for optimizing regional ecosystem services and developing a land-use structure consistent with sustainable development strategies. We propose a framework of a coupled system dynamics (SD) model, patch generation land-use simulation (PLUS) model, and integrated valuation of ecosystem services and trade-offs (InVEST) model to dynamically simulate the spatial and temporal changes of land use and land-cover change (LUCC) and ecosystem carbon stocks under the NDS (natural development scenario), EPS (ecological protection scenario), RES (rapid expansion scenario), and HDS (high-quality development scenario) in Nanjing from 2020 to 2040. From 2005 to 2020, the expansion rate of construction land in Nanjing reached 50.76%, a large amount of ecological land shifted to construction land, and the ecological carbon stock declined dramatically. Compared with 2020, the ecosystem carbon stocks of the EPS and HDS increased by 2.4 × 10⁶ t and 1.5 × 10⁶ t, respectively, with a sizable ecological effect. It has been calculated that forest and cultivated land are the two largest carbon pools in Nanjing, and the conservation of both is decisive for the future carbon stock. It is necessary to focus on enhancing the carbon stock of forest ecosystems while designating differentiated carbon sink enhancement plans based on the characteristics of other land types. Fully realizing the carbon sink potential of each ecological functional area will help Nanjing achieve its carbon neutrality goal. The results of the study not only reveal the challenges of ecological conservation in Nanjing but also provide useful guidance for enhancing the carbon stock of urban terrestrial ecosystems and formulating land-use planning in line with sustainable development strategies.

Land use directly affects the carbon emissions and carbon stock of the ecosystem, and indirectly affects the carbon emissions from anthropogenic activities, which occur more frequently in coastal regions. Taking Nantong City as an example, detailed carbon emission projects were classified and calculated for different land use types by combining land use images of five typical years. Based on the complex relationship between land use carbon emissions and socio-economic factors, the system dynamics model (SD) was used to simulate the land use carbon emissions from 2005 to 2060, and to construct carbon-neutral policy scenarios. Compared with inlands, carbon emissions from land use in Nantong are more pronounced than inland areas, and unique land use types, such as shallows, play an important role as carbon sinks. Total land use carbon emissions show an upward trend from 2005 to 2020 and carbon emissions from construction land dominate. Under the natural development condition, the total net carbon emissions of Nantong are about 4,298,250 tons in 2060, failing to achieve carbon neutrality. The scenario with all four policies adjusted (LO, IO, TP, and PC) has the best emission reductions, peaking at 10,949,010 tons of net carbon emissions in 2029 and reducing them to 1,370,202 tons in 2060, which is the scenario closest to the carbon-neutral target. Overall, this study provides a meaningful conclusion for the study of land use carbon emission characteristics and low-carbon pathways in coastal cities, which can guide the formation of government policies.