Hasil untuk "Industries. Land use. Labor"

Guanghe Han, Jiahui Xu, Xin Zhang et al.

Promoting low-carbon agriculture is vital for climate action and food security. State farms serve as crucial agricultural production bases in China and are essential in reducing China’s carbon emissions and boosting emission efficiency. This study calculates the carbon emissions of state farms across 29 Chinese provinces using the IPCC method from 2010 to 2022. It also evaluates emission efficiency with the Super-Slack-Based Measure (Super-SBM model) and analyzes influencing factors using the Logarithmic Mean Divisia Index (LMDI) method. The findings suggest that the three largest carbon sources are rice planting, chemical fertilizers, and land tillage. Secondly, agricultural carbon emissions in state farms initially surge, stabilize with fluctuations, and ultimately decline, with higher emissions observed in northern and eastern China. Thirdly, the rise of agricultural carbon emission efficiency is driven primarily by technological progress. Lastly, economic development and industry structure promote agricultural carbon emissions, while production efficiency and labor scale reduce them. To reduce carbon emissions from state farms in China and improve agricultural carbon emission efficiency, the following measures can be taken: (1) Improve agricultural production efficiency and reduce carbon emissions in all links; (2) Optimize the agricultural industrial structure and promote the coordinated development of agriculture; (3) Reduce the agricultural labor scale and promote the specialization, professionalization, and high-quality development of agricultural labor; (4) Accelerate agricultural green technology innovation and guide the green transformation of state farms. This study enriches the theoretical foundation of low-carbon agriculture and develops a framework for assessing carbon emissions in Chinese state farms, offering guidance for future research and policy development in sustainable agriculture.

Anderson Nunes de Carvalho Vieira, Armin Feiden

Este estudo analisa a produção de biodiesel em Mato Grosso entre 2006 e 2024, com foco nos fatores econômicos e produtivos que sustentam o monopólio da soja como principal matéria-prima. A pesquisa se se justifica pela relevância do estado como maior produtor de soja do Brasil e pela necessidade de diversificar a matriz agroenergética, mitigando riscos econômicos e ambientais associados à dependência de uma única fonte. O Trabalho adota uma abordagem mista, com análise quantitativa fundamentada em regressão linear múltipla e métodos econométricos, como testes de multicolinearidade, heterocedasticidade e autocorrelação, além de análise qualitativa para contextualizar os achados. Os resultados indicaram que o custo de produção da soja e o aumento dos percentuais de biodiesel têm correlação positiva e significativa com a produção de biodiesel, refletindo maior eficiência produtiva e políticas públicas favoráveis. Por outro lado, o preço da saca de soja apresentou correlação negativa, sugerindo que o aumento nos custos da matéria-prima o que desestimula sua utilização. Concluiu-se que a dependência da soja como matéria-prima está profundamente enraizada em fatores econômicos e estruturais, mas estratégias de diversificação e inovação são essenciais para aumentar a resiliência e sustentabilidade do setor de biodiesel em Mato Grosso.

Shah Fahad, Jianling Wang

Pakistan is the world’s most susceptible country to extreme climatic events, such as floods and droughts. This study aims to investigate the risks related to climate variability and the adaptation measures utilized by farm households in their farms to cope with the adverse shocks of climatic disasters. A dataset of 600 respondents was collected using structured questionnaire from four districts namely Charsadda, Mardan, Nowshera and Peshawar of Khyber Pakhtunkhwa province of Pakistan. Findings of the research showed that soil fertility loss, water scarcity, changes in crop yields and crop diseases were the main determinants of climate variability. Further study participants were also utilizing several adaptation techniques such as change in crop type and variety, change fertilizer, seed quality, pesticide, plant shade trees; water storage and farm diversification. Results of our study further showed that in the study area, study participants were facing various constraints in adoption of certain adaptation measures to deal with climate variability, such as shortage of labor, insecure land tenure system, lack of market access, poverty, land of governmental support, lack of access to assets, lack of water sources, lack of credit sources and lack of knowledge and information were the main constraints faced by the farm households. Findings of this research provide useful insights to the responsible authorities for policy implementation. Our study further suggests that the government should provide proper support to the farmers in the shape of access to farm inputs, access to information and extension services on climate variability and adaptation.

Degui Yu, Ying Cao, Suyan Tian et al.

In order to alleviate the constraints of global warming and sustainable development, digitalization has made significant contributions to promoting agricultural carbon reduction through resources, technology, and platforms. Under this situation, China insists on developing agricultural scale management. However, what impact will scale management in agricultural digital emission reduction have on mechanisms and pathways? Based on three rounds of follow-up surveys conducted by the Digital Countryside Research Institute of Nanjing Agricultural University in Jiangsu Province from 2022 to 2024, in this study a total of 258 valid questionnaires on the rice and wheat industry were collected. Methods such as member checking and audit trail were employed to ensure data reliability and validity. Using econometric approaches including Tobit, mediation, and moderation models, this study quantified the Scale Management Level (SML), examined the mechanism pathways of digital emission reduction in a scaled environment, further demonstrated the impact of scale management on digital emission reduction, and verified the mediating and moderating effects of internal and external scale management. We found that: (1) In scale and carbon reduction, the SBM-DEA model calculates that the scale of agricultural land in Jiangsu showed an “inverted S” trend with SML and an “inverted W” trend with the overall agricultural green production efficiency (AGPE), and the highest agricultural green production efficiency is 0.814 in the moderate scale range of 20–36.667 hm2. (2) In digitalization and carbon reduction, the Tobit regression model results indicate that Network Platform Empowerment (NPE) significantly promotes carbon reduction (p DRE > DTE. (3) Adding scale in agricultural digital emission reduction, the intermediary mechanism results showed that the significant intensity (p DTE > DRE, and that of the Employment of Labor (EOL) is DRE > NPE > DTE. (4) Adding scale in agricultural digital emission reduction, the regulatory effect results showed that the Organized Management Level (OML) and Social Service System (SSS) significantly positively regulate the inhibitory effect of DRE and DTE on AGPE. Finally, we suggest controlling the scale of land management reasonably and developing moderate agricultural scale management according to local conditions, enhancing the digital literacy and agricultural machinery training of scale entities while encouraging the improvement of organizational level and social service innovation, and reasonably reducing labor and mechanization inputs in order to standardize the digital emission reduction effect of agriculture under the background of scale.

Shaoming Han, Cheng Qian, Nawal Abdalla Adam et al.

This study examines the impact of financial inclusion on bank stability across 36 emerging economies, utilizing bank-level data from over 1,500 commercial banks spanning the period 2004 to 2023. Despite the recognized benefits of financial inclusion, its influence on banking stability remains complex and context dependent. The research employs advanced econometric methodologies, including fixed-effects models, Driscoll-Kraay standard errors to address heteroskedasticity and cross-sectional dependence, and system Generalized Method of Moments (GMM) estimation to control for endogeneity and dynamic effects. The findings reveal that financial inclusion generally enhances bank stability and positively influences operational efficiency and funding stability. However, during periods of lax financial regulations or excessive government intervention, banks may engage in riskier behaviors, potentially undermining stability. Key results indicate that (1) robust economic growth and stable policy environments amplify the positive effects of financial inclusion on bank stability, (2) excessive government control may foster risk-taking behaviors, (3) strong financial conditions mitigate adverse impacts, (4) financial inclusion improves risk management and operational efficiency, and (5) effective regulatory frameworks are pivotal in leveraging financial inclusion for sound banking operations. These insights suggest that policymakers in emerging markets should carefully balance the promotion of financial inclusion with safeguards that maintain financial stability.

Anais Galdin, Jesse Silbert

Large language models (LLMs) like ChatGPT have significantly lowered the cost of producing written content. This paper studies how LLMs, through lowering writing costs, disrupt markets that traditionally relied on writing as a costly signal of quality (e.g., job applications, college essays). Using data from Freelancer.com, a major digital labor platform, we explore the effects of LLMs' disruption of labor market signaling on equilibrium market outcomes. We develop a novel LLM-based measure to quantify the extent to which an application is tailored to a given job posting. Taking the measure to the data, we find that employers have a high willingness to pay for workers with more customized applications in the period before LLMs are introduced, but not after. To isolate and quantify the effect of LLMs' disruption of signaling on equilibrium outcomes, we develop and estimate a structural model of labor market signaling, in which workers invest costly effort to produce noisy signals that predict their ability in equilibrium. We use the estimated model to simulate a counterfactual equilibrium in which LLMs render written applications useless in signaling workers' ability. Without costly signaling, employers are less able to identify high-ability workers, causing the market to become significantly less meritocratic: compared to the pre-LLM equilibrium, workers in the top quintile of the ability distribution are hired 19% less often, workers in the bottom quintile are hired 14% more often.

Qing Xiao, Rongyi Chen, Jingjia Xiao et al.

This study examines the discursive construction of algorithms and its role in labor management in Chinese live-streaming industry by focusing on how intermediary organizations (Multi-Channel Networks, MCNs) actively construct, stabilize, and deploy particular interpretations of platform algorithms as instruments of labor management. Drawing on a nine-month ethnographic fieldwork and 44 interviews with live-streamers, former live-streamers, and MCN staff, we examine how MCNs produce and circulate structured interpretations of platform algorithms across organizational settings. We show that MCNs articulate two asymmetric yet interconnected forms of algorithmic interpretations. Internally, MCNs managers approach algorithms as volatile and uncertain systems and adopt probabilistic strategies to manage performance and risk. Externally, in interactions with streamers, MCNs circulate simplified and prescriptive algorithmic narratives that frame platform systems as transparent, fair, and responsive to individual effort. These organizationally produced algorithmic interpretations are embedded into training materials, live-streaming performance metrics, and everyday management practices. Through these mechanisms, streamers internalize responsibility for outcomes, intensify self-discipline, and increase investments in equipment, performing skills, and routines to maintain streamer-audience relationship, while accountability for unpredictable outcomes is increasingly shifted away from managers and platforms. This study contributes to CSCW and platform labor research by demonstrating how discursively constructed algorithmic knowledge can function as an intermediary infrastructure of soft control, shaping how platform labor is regulated, moralized, and governed in practice.

Hugo Spring-Ragain

This paper develops a spatial-dynamic framework to analyze the theoretical and quantitative effects of a Land Value Tax (LVT) on urban land markets, capital accumulation, and spatial redistribution. Building upon the Georgist distinction between produced value and unearned rent, the model departs from the static equilibrium tradition by introducing an explicit diffusion process for land values and a local investment dynamic governed by profitability thresholds. Land value $V (x, y, t)$ and built capital $K(x, y, t)$evolve over a two-dimensional urban domain according to coupled nonlinear partial differential equations, incorporating local productivity $A(x, y)$, centrality effects $μ(x, y)$, depreciation $δ$, and fiscal pressure $τ$ . Analytical characterization of the steady states reveals a transcritical bifurcation in the parameter $τ$ , separating inactive (low-investment) and active (self-sustaining) spatial regimes. The equilibrium pair $(V ^*, K^*)$ is shown to exist only when the effective decay rate $α= r + τ- μ(x, y)$ exceeds a profitability threshold $θ= κ+ δ/ I_0$, and becomes locally unstable beyond this boundary. The introduction of diffusion, $D_V ΔV$, stabilizes spatial dynamics and generates continuous gradients of land value and capital intensity, mitigating speculative clustering while preserving productive incentives. Numerical simulations confirm these analytical properties and display the emergence of spatially heterogeneous steady states driven by urban centrality and local productivity. The model also quantifies key aggregate outcomes, including dynamic tax revenues, adjusted capital-to-land ratios, and net present values under spatial heterogeneity and temporal discounting. Sensitivity analyses demonstrate that the main qualitative mechanisms-critical activation, spatial recomposition, and bifurcation structure-remain robust under alternative spatial profiles $(A, μ)$, discretization schemes, and moderate differentiation of the tax rate $τ(x, y)$. From an economic perspective, the results clarify the dual nature of the LVT: while it erodes unproductive rents and speculative land holding, its dynamic incidence on built capital depends on local profitability and financing constraints. The taxation parameter $τ$ thus acts as a control variable in a nonlinear spatial system, shaping transitions between rent-driven and production-driven equilibria. Within a critical range around $τ_c$, the LVT functions as an efficient spatial reallocation operator-reducing inequality in land values and investment density without impairing aggregate productivity. Beyond this range, excessive taxation induces systemic contraction and investment stagnation. Overall, this research bridges static urban tax theory with dynamic spatial economics by formalizing how a land-based fiscal instrument can reshape the geography of value creation through endogenous diffusion and nonlinear feedback. The framework provides a foundation for future extensions involving stochastic shocks, adaptive policy feedbacks, or endogenous public investment, offering a unified quantitative perspective on the dynamic efficiency and spatial equity of land value taxation.

Maksim Papenkov

Accurate industry classification is critical for many areas of portfolio management, yet the traditional single-industry framework of the Global Industry Classification Standard (GICS) struggles to comprehensively represent risk for highly diversified multi-sector conglomerates like Amazon. Previously, we introduced the Multi-Industry Simplex (MIS), a probabilistic extension of GICS that utilizes topic modeling, a natural language processing approach. Although our initial version, MIS-1, was able to improve upon GICS by providing multi-industry representations, it relied on an overly simple architecture that required prior knowledge about the number of industries and relied on the unrealistic assumption that industries are uncorrelated and independent over time. We improve upon this model with MIS-2, which addresses three key limitations of MIS-1 : we utilize Bayesian Non-Parametrics to automatically infer the number of industries from data, we employ Markov Updating to account for industries that change over time, and we adjust for correlated and hierarchical industries allowing for both broad and niche industries (similar to GICS). Further, we provide an out-of-sample test directly comparing MIS-2 and GICS on the basis of future correlation prediction, where we find evidence that MIS-2 provides a measurable improvement over GICS. MIS-2 provides portfolio managers with a more robust tool for industry classification, empowering them to more effectively identify and manage risk, particularly around multi-sector conglomerates in a rapidly evolving market in which new industries periodically emerge.

J. Groen, Mark J. Kutzbach, Anne E. Polivka

Hurricanes Katrina and Rita devastated the US Gulf Coast in 2005. We use job-level data to compare the evolution of earnings for affected workers in four states with workers from matched control counties. We attribute short-term earnings losses to job separations and long-term gains to wage growth in the affected areas. Wages rose due to reduced labor supply and increased labor demand in the affected labor markets. Damage to a worker’s residence or workplace accentuated short-term earnings losses. Effects varied by prestorm industry, with larger gains for workers in sectors related to rebuilding.

G. Snashall, H. Poulos

De-gen Wang, Yujia Zhu, Mei-feng Zhao et al.

Abstract The hollowing of traditional villages not only causes the waste of land resources, loss of population, aging and weakening of rural population, and the decline of rural industries, but also threatens the protection of tangible cultural heritage and traditional folklore. Taking the case of Dongcun Village, a traditional village in Jinting town in Suzhou, this paper measures the degree of hollowing from three dimensions of land, population and industry, and uses GIS technology to analyze the rural hollowing characteristics. It builds regression models with the rural households as the study units and provides a micro-scale analysis of the formation mechanism of traditional village hollowing. The results are shown as follows. (1) The land hollowing rate of this traditional village is 20.19 % in Dongcun Village. Vacant and abandoned residential land is concentrated at the core of the village, while new houses increase on the periphery. Many families have more than one plot of housing land, accounting for 67.97 %. (2) The population hollowing of Dongcun Village is not only manifested in the large proportion of out-migrants (20.3 %), but also in the unbalanced structure of the resident population. The proportion of remaining labor has decreased to 42.31 % and is lower than the average level of rural China. (3) More than half of the households only had the elderly and weak farming laborers, and a few households even abandoned it. Industry hollowing was particularly severe in households along the town road, indicating that the periphery of the village was not solid and some deeper problems of population and industry hollowing occurred in there. (4) The hollowing of traditional villages is influenced by various factors including family economics, location and transportation, natural resource endowment, family demographic structure, housing situation, and land management. Among them, family economy is the main influence factor of rural hollowing, since non-agricultural employment transformation enhances the ability of farmers to build houses, contributing to land and industry hollowing. The location and transportation factor is the guiding force for the hollowing of land and industry. The relative lack of cultivated land resources and the low efficiency of farming are the root causes of population and industry hollowing. The family demographic structure provides the basic driving force for rural hollowing. The housing situation, especially the building year of houses, affects the demand for house renewal and becomes the direct driving force of land hollowing. The land management factor also contributes to hollowing.

Rayhan Rizki Adzani, M. Arif

The palm oil processing industry is one that is believed to be able to help the Indonesian state in increasing per capita income, providing employment, reducing poverty, and as a foreign exchange income. When compared to other vegetable oils like coconut, soybean, or flower oil, palm oil offers advantages sun. This study's goal was to examine the factors impacting the province of West Kalimantan's palm oil production yield between 2015 to 2021. This research uses a scientific techniques and a type of quantitative descriptive research. Secondary sources of data were used in this research. This research's data was obtained between the years of 2015 and 2021 from the websites of the West Kalimantan Province Agricultural Department and the BPS (Central Statistics Administration). In this study, regression with panel data analysis was the analytical technique. The independent factors in this study are land area, labor, and rainfall, while the dependent variable is palm oil production. The outcomes obtained from the panel data regression analysis are land area has a positive effect on oil palm production and rainfall has a positive effect on oil palm production. Meanwhile, labor does not affect palm oil production.

Tikuneh Dessye, Woldesenbet Laike

The domestic rice industry of Ethiopia is constrained by low productivity, poor quality, and old processing machines. The rice production system is done by hand or with rudimentary tools, and only 2% of households have access to tractors. It takes 175 labor days to weed and 66% of the total farm operations. Rice harvesting and threshing are done manually using a serrated sickle and animal trembling respectively. Farmers are responsible for most of the pre-milling operations and store paddy for household consumption in local stores. Challenges include fragmented farm holdings, poor marketing channels, and a lack of awareness of post-harvest utilization. The prospects for rice mechanization development include improving the rice mechanization research system, training local entrepreneurs, providing repair and maintenance services, promoting custom hiring centers, local manufacturing of farm implements, organizing agricultural cooperatives, landholding, and land ownership structures, assessing foreign experience, linking importers and service providers, and encouraging investments in the rural infrastructure.

Shivam Pande

The study explores the synergistic combination of Synthetic Aperture Radar (SAR) and Visible-Near Infrared-Short Wave Infrared (VNIR-SWIR) imageries for land use/land cover (LULC) classification. Image fusion, employing Bayesian fusion, merges SAR texture bands with VNIR-SWIR imageries. The research aims to investigate the impact of this fusion on LULC classification. Despite the popularity of random forests for supervised classification, their limitations, such as suboptimal performance with fewer features and accuracy stagnation, are addressed. To overcome these issues, ensembles of random forests (RFE) are created, introducing random rotations using the Forest-RC algorithm. Three rotation approaches: principal component analysis (PCA), sparse random rotation (SRP) matrix, and complete random rotation (CRP) matrix are employed. Sentinel-1 SAR data and Sentinel-2 VNIR-SWIR data from the IIT-Kanpur region constitute the training datasets, including SAR, SAR with texture, VNIR-SWIR, VNIR-SWIR with texture, and fused VNIR-SWIR with texture. The study evaluates classifier efficacy, explores the impact of SAR and VNIR-SWIR fusion on classification, and significantly enhances the execution speed of Bayesian fusion code. The SRP-based RFE outperforms other ensembles for the first two datasets, yielding average overall kappa values of 61.80% and 68.18%, while the CRP-based RFE excels for the last three datasets with average overall kappa values of 95.99%, 96.93%, and 96.30%. The fourth dataset achieves the highest overall kappa of 96.93%. Furthermore, incorporating texture with SAR bands results in a maximum overall kappa increment of 10.00%, while adding texture to VNIR-SWIR bands yields a maximum increment of approximately 3.45%.

Arezoo Jafari, Priscila De Azevedo Drummond, Dominic Nishigaya et al.

Agricultural workers are essential to the supply chain for our daily food and yet, many face harmful work conditions, including garnished wages, and other labor violations. Workers on H-2A visas are particularly vulnerable due to the precarity of their immigration status being tied to their employer. Although worksite inspections are one mechanism to detect such violations, many labor violations affecting agricultural workers go undetected due to limited inspection resources. In this study, we identify multiple state and industry level factors that correlate with H-2A violations identified by the U.S. Department of Labor Wage and Hour Division using a multilevel zero-inflated negative binomial model. We find that three state-level factors (average farm acreage size, the number of agricultural establishments with less than 20 employees, and higher poverty rates) are correlated with H-2A violations. These findings provide guidance for inspection agencies regarding how to prioritize their limited resources to more effectively inspect agricultural workplaces, thereby improving workplace conditions for H-2A workers.

Dilek Tokel, I. Dogan, Asli Hocaoglu-Ozyigit et al.

ABSTRACT Cotton is a product that provides wealth for humanity with its widespread and compulsory usage, and alongside creating employment opportunities in the producer countries. Due to the availability of suitable land for agriculture, seven countries including Turkey produce close to 80% of cotton worldwide. The average cotton yield in Turkey is above the world average, placing the country close to first place among the major producing countries. On the other hand, Turkey is one of the main countries engaged in organic cotton production by using only non-transgenic seeds and therefore, its cotton as a brand is registered as “GMO Free Turkish Cotton”. This provides a privileged position to the country as “the only country in the world producing GMO-free cotton”. Also, the Turkish textile industry is a driving sector for the country in terms of producing high-quality products, bringing foreign exchange to the country and creating a large labor-intensive workforce for the country. In this study, the state of cotton affairs was discussed in detail using relevant data along with its trade and importance in the textile industry in the world and Turkey.

Qixing Huang, Xiaoping Zheng, Ruimei Wang

Non-agricultural employment plays a significant role in alleviating regional poverty. Using the micro data of the China Labor-Dynamics Survey (CLDS), this paper empirically analyzes the impact of the accessibility of rural transportation infrastructure on the non-agricultural employment choices of rural laborers by using the entropy method and the ordered Logit model. The results show that there is a significant positive correlation between the accessibility of rural transportation infrastructure and the non-agricultural employment of rural laborers. The study also finds that the laborers participating in non-agricultural employment in villages with good transportation infrastructure will prefer to be employed in nearby locations, and the development of the rural non-agricultural economy is an important reason. Further analysis clearly shows that gender, the family dependency ratio, and rural terrain characteristics affect the choices made by laborers with respect to non-agricultural employment. Based on the research results, focusing on a transportation and industry model and considering the construction of transportation infrastructure as a guide, especially in areas with poor terrain, promoting the development of rural non-agricultural industries can help solve the problem in rural areas and in women’s employment where family members or accompanying personnel are left behind, and can promote the orderly transfer of rural laborers.

J. Samtani, C. Rom, H. Friedrich et al.

Strawberry (Fragaria ×ananassa) production practices followed by growers in the United States vary by region. Understanding the challenges, needs, and opportunities in each region is essential to guide research, policy, and marketing strategies for the strawberry industry across the country, and to enable the development of general and region-specific educational and production tools. This review divided the United States into eight distinct geographic regions and an indoor controlled or protected environment production system. Current production systems, markets, cultivars, trends, and future directions for each region are discussed. A common trend across all regions is the increasing use of protected culture strawberry production with both day-neutral and short-day cultivars for season extension to meet consumer demand for year-round availability. All regions experience challenges with pests and obtaining adequate harvest labor. Increasing consumer demand for berries, climate change-induced weather variability, high pesticide use, labor and immigration policies, and land availability impact regional production, thus facilitating the adoption of new technologies such as robotics and network communications to assist with strawberry harvesting in open-field production and production under controlled-environment agriculture and protected culture.

Suri Guga, Jie Xu, Dao Riao et al.

Abstract Guangxi is the primary producer of sugarcane in China and provides a highly suitable habitat for sugarcane growth. However, its distribution range has changed significantly in recent years due to climate change as well as human factors. Without extensive knowledge of the changing trends in suitable sugarcane planting areas, efforts to improve its productivity in Guangxi may be insufficient. In this study, the interdecadal change in sugarcane distribution in Guangxi in response to climate change from 1960 to 2019 was estimated using the MaxEnt model and the landscape pattern of land use in the suitable sugarcane area was analyzed. In addition, we discuss the effects of global warming on sugarcane production in the sustainable development of the sugar industry in Guangxi. Our results indicate: (1) from 1960 to 2019, approximately 65% of Guangxi Province could grow sugarcane. Chongzuo City, Nanning City and Parts of Baise City, are highly suitable areas, and unsuitable areas are mainly concentrated in the north. In general, sugarcane climate suitability extended further in low-altitude areas, and then extended to high- altitude areas. However, from the 2000s to the 2010s, climate suitability showed a decreasing trend, decreasing from 16.036 × 106 ha to 15.4985 × 106 ha (2) The order of land use area in the suitable sugarcane climate range was as follows: woodland > cropland > grassland > construction land > water. With the increase in climate suitability, the distribution of cultivated land expanded. From 1980 to 2005, cropland in suitable areas showed a fragmentation trend. By 2010, the cropland patches disappeared after fragmentation. (3) Due to landscape constraints, infertile soil, and labor costs, the sugar industry faces various challenges. The evaluation of climate suitability could provide a theoretical reference for a planting layout of sugarcane, and landscape pattern analysis of suitable sugarcane climate areas is conducive to the integration of small pieces of land into large ones, making mechanization possible. Overall, strict layout and management measures are required in sugarcane planting areas.