Development Economics Research Articles

Lembaga Filantropi dan Fiskal Sebagai Instrumen Pembangunan Ekonomi Negara

Economic development is the main goal that every country wants to achieve to improve people's welfare, reduce poverty, and encourage sustainable economic growth. This study examines the important role of philanthropic institutions and fiscal policy in encouraging economic development, both in general and from an Islamic economic perspective. Philanthropy, through voluntary contributions such as zakat, infaq, alms, and waqf, helps fill gaps that cannot be fully addressed by the public sector, improves people's welfare, and reduces socio-economic disparities. Fiscal policy, as the main tool of government, plays an important role in managing state revenues and expenditures, influencing economic growth, income distribution, and macroeconomic stability. Through in-depth literature analysis, this research finds synergies between the philanthropic sector and fiscal policy that can strengthen sustainable economic development. This study also provides insight into the development of Islamic economics in the last three decades, showing significant progress in academic research and operational practice. It is hoped that the results of this research can provide significant policy recommendations to maximize the potential of these two tools in achieving fair and sustainable economic development.

Unleashing Economic Potential: Enhancing Cassava Production Efficiency in Nigeria

Purpose: This study is aimed at determining the economic efficiency of Manihot esculenta Crantz (Cassava) production in Nigeria’s Delta State. The study offers a detailed analysis of how socio-economic characteristics influence production output across both early and late farming seasons. Design/ Methodology/ Approach: This study employed a multi-stage random sampling approach in selecting a diverse cohort of 500 cassava farmers engaged in both early and late-season cultivation. Through rigorous personal interviews, primary data was gathered that was both comprehensive and insightful. Advanced parametric statistics and the Cobb-Douglas stochastic frontier function were used for the analysis. Findings: The mean economic efficiency scores were determined to be 0.63 for early-season farms, 0.78 for late-season farms, and 0.76 when considering all farms collectively. The output elasticities of material inputs, labour, and land were found to be 0.42, 0.61, and 0.05, respectively for all farms. For early-season farms, these values were 0.36, 0.68, and 0.04, whereas, for late-season farms, they were 0.31, 0.7, and 0.03. Research Limitation/Implications: This work concentrated on Cassava production efficiency within and around Nigeria’s Delta area. Practical Implication: Understanding the socio-economic factors that influence production during different seasons can help in designing strategies that maximize output in both early and late farming seasons. Social Implication: This study would help agricultural development economists and policy-makers in addressing the economic problems of Cassava farmers both present and future generations. Originality/Value: By examining both early and late farming seasons, the study provides a comprehensive understanding of how socio-economic factors vary across different agricultural cycles. This dual-season approach is relatively rare and offers more detailed insights into the dynamics of cassava production throughout the year.

Comparison of the Impact of Different Economic Patterns on Population Inflows: Evidence from China’s Guangdong, Jiangsu, and Zhejiang Provinces

Between 2000 and 2022, China’s top three highest GDP provinces, Guangdong, Zhejiang, and Jiangsu, each having distinct economic structures, displayed different paths of development in their net population inflows. This prompts us to ponder how the economic patterns of the most economically developed regions impact population inflows. To answer the question, we first examine each economic pattern and use the entropy weight method to construct a comprehensive index to capture the features of each economic pattern in different regions. Then, we employ a two-way fixed effects model with panel data from the three provinces to conduct the empirical analysis. Moving forward, we expand the sample size to 10 provinces, including China’s eastern metropolitan areas, to extend the analysis beyond the previously selected regions and corroborate the consistency and robustness of our model. The results show that the Wenzhou pattern, featured primarily by the private sector, has the most impact on population inflows, followed by the Pearl River pattern, driven by an export-oriented economy. In contrast, the Sunan pattern, characterized by the collective economy, has an insignificant impact. We further dissect and determine the essential factors influencing population inflows within the three economic patterns and estimate the sustainability of the economic pattern via net population inflows. Our findings can provide insights for policy-makers to understand and utilize economic patterns in order to impact population inflows effectively. Specifically, we propose that the observable net population inflows can serve as an indicator to evaluate the sustainability of local economic patterns, thus providing another perspective on assessing the region’s economic development.

Spatio-temporal evolution analysis of land use change and landscape ecological risks in rapidly urbanizing areas based on Multi-Situation simulation − a case study of Chengdu Plain

Land use change in emerging nations raises landscape ecological risks (LERS), hastens the deterioration of urban and rural ecosystem services, endangers human well-being, and undermines sustainable development in the face of rapidly increasing urbanization. Here, using the Chengdu Plain as the study area and long time series data from 2000 to 2020, the optimal time span is selected for multi-context spatio-temporal simulation. The land use map of Chengdu Plain in 2025 under the four scenarios of “Natural Development” (ND), “Economic Priority Development” (END), “Ecological Priority Development” (ELD), and “Sustainable Development” (SD) was simulated, and the multi-indicator landscape ecological risk index (ERI) was generated to compare and analyze the differences between land use and landscape ecological risk under different policy preferences. Subsequently, the land use data from 2025 to 2040 were simulated, the landscape ecological risk pattern was mapped, and the spatial and temporal evolution analysis from 2010 to 2040 was conducted to explore the spatial evolution law of land use change and landscape ecological risk. Based on the results, the high ecological risk aggregation areas are prone to appear in END scenarios, whereas medium-ecological risk aggregation areas are more likely to appear in ELD scenarios, and the government should focus its policy on arable land protection. Moreover, the land use pattern of cultivated land surrounding construction land and forested land surrounding cultivated land, caused by the irrational single-core development pattern and the policy of returning farmland to forests, has exacerbated the landscape ecological risk of the Chengdu Plain, constituting a unique landscape ecological risk pattern. It’s also important to remember that the Chengdu Plain’s less economically developed regions need to focus on the high-quality development of ecological land use. We adopted high-precision simulation methods to simulate the complex land use changes in rapidly urbanizing areas and explored the spatial evolution law and causes of landscape ecological risk evolution in the context of land use changes, with the intention of offering a solid theoretical foundation for such areas’ future planning in developing nations.

What combinations drive the urban green infrastructure development in China’s Yangtze River Economic Belt? − An empirical study based on fs/QCA methodology

In cities, green infrastructure (GI) is a vital natural life support system that is intimately linked to the daily lives of citizens. High levels of Urban Green Infrastructure (UGI) can help mitigate a series of ecological problems faced by cities, which is extremely important for preserving the stability of urban ecosystems and promoting sustainable urban development. However, current research has not fully explored the influencing factors of UGI and the interaction effect between them. Therefore, in order to better explore the results of China’s proposed policy to promote GI development, data from 110 cities in China’s Yangtze River Economic Belt (YREB) region during the 12th Five-Year Plan and 13th Five-Year Plan periods were measured separately, and natural and geographic environmental conditions, quality of urban construction, level of economic development, socio-cultural development, and eco-environmental quality were selected as the influencing factors. The entropy TOPSIS was used to evaluate the development trend of UGI in the two periods of the region, and the relationship between influencing factors and UGI development was investigated through the use of fs/QCA, and the high-level development path of each city to achieve GI was explained. The results show that (1) Cities with high UGI development are mostly concentrated in economically developed areas, while cities in the YREB’s central and western regions have relatively low levels of UGI development. (2) According to the spatial and temporal differences, the factors driving the high level and non-high level of UGI development in prefecture-level cities in the YREB showed several different configurations, confirming that the development level of UGI was affected by a variety of conditions. (3) During the 12th Five-Year Plan to 13th Five-Year Plan periods, the configuration of urban high-level UGI was found to be more consistent, while the development path of non-high-level UGI is more diversified. The study explores the overall development level and trend of green infrastructure in different cities, and the citation of fs/QCA provides a better understanding of the complex mechanisms of the interactions between these variables, with a view to providing some guidance and enlightenment for enhancing the future urban UGI development and urban high-quality development in the Yangtze River Economic Belt.

Deciphering the digital divide: the heterogeneous and nonlinear influence of digital economy on urban-rural income inequality in China

ABSTRACT This study investigates the heterogeneous and nonlinear impact of digital economy on the urban-rural income disparities in 30 Chinese provinces from 2013 to 2020. The results from moderated multiple regressions and panel threshold regressions consistently indicate that digital economy generally widens China’s urban-rural inequality, but this widening effect diminishes as the digital economy progresses. Furthermore, the influence of digital economy varies depending on local socio-economic factors, including economic development, industrial structure, education level, government expenditure, and openness. Digital economy exerts a stronger influence on exacerbating the inequality in economically developed regions with higher education levels and greater openness, but it helps prevent the inequality from expanding through its interactions with secondary industry development and fiscal expenditure. These findings provide a reference for formulating target strategies in line with local development conditions to bridge the inequality engendered by the digital divide.

Falling into the trap: A study of the cognitive neural mechanisms of immediate rewards impact on consumer attitudes toward forwarding perk advertisements.

In the context of digital marketing, consumers often express aversion to perk advertisements yet find it challenging to resist the temptation and forward it, resulting in inconsistent attitudes and behaviors. This study, based on the Associative Propositional Evaluation model and the Confirmation Bias theory, utilizes event-related potential experiments to identify the interactive impacts of immediate rewards and information diagnosticity in advertisements on consumer attitude change in specific contexts. The research findings indicate that when rewards were present, information diagnosticity positively influences attitude change and the willingness to forward. However, when rewards were absent, the impact of information diagnosticity on attitude change and the willingness to forward is not significant, and neuroscientific evidence supports these findings. Theoretically, this study extends the research perspective on attitude change in online advertising contexts and broadens the application of the Associative Propositional Evaluation model in the field of consumer attitude change towards advertisements. In practice, this research holds significant guiding value for constraining platform manipulation of consumer cognitive behaviors, guiding the healthy development of platform economics, and promoting digital technology ethics.

Analysis of Policy Options Based on Data-Driven Economic Cycles and Industrial Structure Upgrading

China's economy has achieved a high growth rate of 9.8% in cyclical fluctuations, and the industrial structure has been continuously improved with growth. However, the irrationality of the tertiary industry structure and its internal structure still restricts the sustainable development. The optimization of the industrial structure depends on many factors, such as government policies, economic growth mode, resource constraints, economic development stage and economic cycle stage. Based on data-driven analysis, this paper analyzes the general path and policy choice of economic cycle to adjust China's industrial structure, and the impact of economic cycle on the upgrading of industrial structure. After the actual analysis, we found that the threshold of economic growth in economically developed regions is high, the role of financial development in stimulating industrial structure is not prominent, and industrial upgrading is relatively difficult. Industrial upgrading is difficult.

Coupling and interaction mechanism between green urbanization and tourism competitiveness based an empirical study in the Yellow River Basin of China

Exploring the spatial coupling relationship and interaction mechanism between green urbanization (GU) and tourism competitiveness (TC) is of great significance for promoting urban sustainable development. However, the lack of research on the interaction mechanism between GU and TC limits the formulation of effective environmental management policy and urban planning. Taking 734 counties in the Yellow River Basin (YRB) as the study area, this paper analyzes the spatial coupling relationship between GU and TC on the basis of comprehensive evaluation of GU and TC. Then, the interactive mechanism between GU and TC is systematically discussed, and the synergistic development strategy of the two is proposed. The results show that the GU level presents a multicore circle structure, with provincial capitals, prefecture-level urban districts and economically developed counties in east-central regions as high-value centers. The TC at county scale presents a multi-center spatial structure. Additionally, there is a significant positive spatial coupling between GU and TC in the YRB. The analysis further reveals that green urbanization level, social progress, population development, infrastructure construction, economic development quality, and eco-environmental protection has a observably influence on TC. Tourism competitiveness, service competitiveness, location competitiveness, resource competitiveness, market competitiveness, environmental influence, and talent competitiveness has a observably influence on GU. TC can promote GU, and the improvement of green urbanization level can support the development of tourism competitiveness. According to the spatial zoning method, 734 counties are divided into 6 categories, and the coordinated development strategy of GU and TC for each type of district is proposed.

Spatiotemporal dynamics and determinants of medical service efficiency in China

BackgroundMedical service efficiency is an important indicator for measuring the equity of medical services. Therefore, this study primarily focuses on investigating the spatiotemporal domain to explore both spatial and temporal characteristics, as well as influencing factors that affect medical service efficiency across diverse provinces in China.MethodsThe super Epsilon-based Measure (EBM) unexpected model has previously been utilized to quantify energy eco-efficiency, carbon emission efficiency, and green development efficiency. However, limited studies have applied this method to assess the efficiency of healthcare services. Therefore, this study investigates the application of the super-EBM-unexpected model in evaluating medical service efficiency, and further integrates spatial econometric models to explore the influencing factors of medical service efficiency and aims to identify potential avenues for improvement.ResultsThe average efficiency of medical services in the 31 provinces of China ranges from 0.6 to 0.7, indicating predominantly low efficiency values. However, economically developed coastal areas exhibit relatively high efficiency levels above 1. Conversely, regions with relatively lower levels of economic development demonstrate lower efficiency rates at approximately 0.3. Evidently, substantial regional disparities exist. For the influencing factors, the enhancement of residents' living standards can effectively foster the medical service efficiency, while residential living standards of nearby areas can also exert an impact in this region. The influence of educational attainment on medical service efficiency exhibits a significant inhibitory effect.ConclusionsThe majority of China's 31 provinces exhibit suboptimal medical service efficiency, with notable regional disparities. Future policy initiatives should be tailored to address the unique challenges faced by regions with lower levels of economic development, prioritizing enhancements in both the efficacy and quality of their healthcare systems.

Models of quality costs by A. Feigenbaum, J. Juran, F. Crosby in the concept of the development of quality economics

Aim. Consider the specifics and features of quality cost models by A. Feigenbaum, J. Juran, F. Crosby in the context of increasing the efficiency of cost management.Objectives. Describe Joseph M. Juran's model (Juran's Spiral); classify quality costs by J. Juran — A. Feigenbaum; describe the classification of costs by F. Crosby.Methods. This work uses general scientific research methods: deduction, induction, generalization, comparative analysis, synthesis (unification), study and theoretical analysis.Results. In the modern economy, product quality plays a critical role, influencing the competitiveness and stability of the country's economic system, which makes quality management not just a formality, but a strategic necessity. Active implementation and development of quality systems is considered by enterprises as a means to improve reputation, increase market share and strengthen customer loyalty, and such systems help reduce costs by preventing defects and optimizing processes.Conclusions. An analysis of quality cost models was carried out as one of the key methods in increasing the economic efficiency of the organization. In modern quality management practice, special attention is paid not only to the identification and assessment of quality costs, but also to their role in the process of making managerial and technical decisions. This is especially true when planning the production activities of an enterprise. Quality cost analysis is becoming an important tool to help management formulate economically sound decisions.

Research on multiscale OpenStreetMap in China: data quality assessment with EWM-TOPSIS and GDP modeling

ABSTRACT OpenStreetMap (OSM) is a voluntary platform designed to provide free and up-to-date geographic data. Since OSM is based on multisource geographic data provided by the public, the quality of the data has become a concern of researchers. In this study, a unified measure for evaluating OSM data quality was constructed, and the entropy weight method (EWM) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model were used to evaluate the quality of multiscale OSM data in China from 2014 to 2020. In addition to evaluating the data quality, the use of OSM data quality index factors in economic modeling at different spatial scales in China was explored by using a geographic information system (GIS) analysis method and a geographically and temporally weighted regression (GTWR) model. Four machine learning models, SVM, RF, XGBoost and CatBoost, were used to simulate the grid-scale GDP, and the effectiveness of these simulations was discussed. The results showed that (1) the weights of OSM data quality indicator factors vary across different spatial scales. (2) From 2014 to 2020, the quality of national-scale OSM data first increased, then decreased and then gradually stabilized. In addition, the quality of OSM data at the provincial and municipal scales is significantly different, and the distribution is affected by the population and geographical environment. (3) Over time, the spatial clustering characteristics of OSM data quality at different spatial scales in China has continuously strengthened. In addition, the quality of Chinese OSM data displays obvious local spatial autocorrelation characteristics, which are dominated by H-H clustering and L-L clustering. (4) The GTWR model performs well in predicting and revealing the spatiotemporal correlation characteristics between GDP and OSM data quality indicators at the provincial and municipal scales in China. The correlations increase with decreasing spatial scale (provincial to municipal). Moreover, the GDP modeling ability is better in economically underdeveloped Northwest China and economically developed East China. (5) Four machine learning models coupled with road network length completeness, relative linear density, road name attribute completeness, POI name attribute completeness, road network accuracy, road network update frequency, POI update frequency, topological consistency and directional similarity yielded the best grid-scale GDP simulation values. Notably, the CatBoost model provided the best accuracy, and the results further verified that it is feasible to use the proposed OSM data quality index system to predict the regional economic development level in China.

Analysis of spatial patterns and influencing factors of farmland transfer in China based on ESDA-GeoDetector

Farmland transfer is a critical component in facilitating agricultural scale management and improving agricultural production efficiency. This study examines the spatial distribution of farmland transfer in China and identifies the factors influencing it, offering valuable guidance for advancing China’s farmland transfer practices. Through the application of mathematical statistics and GIS spatial analysis, the study investigates changes in spatial patterns related to the scale, rate, mode, and recipients of farmland transfer across China's 30 provinces from 2015 to 2020. Geographical detectors are also employed to identify the key factors influencing the extent and pace of farmland transfer. The study reveals that between 2015 and 2020, China's farmland transfer area increased from 29,789 to 37,638 million hectares. Provinces with abundant farmland resources generally experienced larger farmland transfers, while economically developed regions and major grain-producing areas saw higher rates of farmland transfers. The predominant mode of farmland transfer in China was leasing (subcontracting), accounting for over 80% of the total transferred area. Large-scale grain growers and family farms were significant participants in farmland transfers, acquiring approximately 60.1% of the transferred lands, followed by professional cooperatives (21.5%), enterprises (10.4%), and other entities (7.9%). Key factors influencing the farmland transfer area include the "regional farmland area", the "proportion of family farms supported by loans", and the "proportion of non-agricultural population", with explanatory powers of 0.663, 0.319, and 0.225, respectively. Notably, there is a substantial interaction between the "regional farmland area" and factors such as the "proportion of family farms supported by loans" and the "grain yield per unit area", with explanatory powers reaching 0.957 and 0.901, respectively. These findings offer valuable insights for promoting farmland transfer in agriculturally rich regions. Factors affecting the farmland transfer rate include "grain yield per unit area", "GDP per capita", and the "proportion of non-agricultural population", each with an explanatory power above 0.500. Moreover, their interactive explanatory powers with other indicators exceed 0.600, indicating that provinces with high agricultural productivity or economic development levels are more likely to undergo farmland transfer. The paper concludes by proposing strategies and recommendations to promote farmland transfer in both "large agricultural areas" and "metropolitan suburbs."

Modeling Climate Change Issues in Indonesia Based on Media Headlines

Climate change has become a global issue affecting all countries in the last decades. This phenomenon poses a concern to Indonesia as it is one of the climate change’s epicenters. Various studies have shown that climate change can harm multiple community activities, such as unstable agricultural production, decreased people’s health, and global warming. This study tried to model and analyze climate change topics discussed in the media. Finding hidden topics from texts can provide clues and information regarding public conversation surrounding climate change, such as public thoughts, perceptions, and readiness to mitigate the possible adverse effects of climate change. In order to identify hidden subjects from the corpus, this work modeled climate change issues in Indonesia using the latent Dirichlet allocation (LDA) algorithm to analyze texts from Indonesian media headlines. As many as 7,000 headline data from five online media were collected from 2017 to 2021 using web scraping techniques. The proposed approach produced eight topics related to climate change, which were determined by the highest coherence value of 0.560. Those topics were renewable energy, carbon emissions, environmental management, development economics, international cooperation, policy/regulation, rehabilitation, and disaster. Based on the results, the model could sufficiently describe the theme of discussion in society and photograph public thoughts and the government’s readiness in the form of policies and regulations in dealing with the climate change phenomenon.

Akuntansi Mudharabah

In line with the development of sharia economics, sharia financial institutions in managing their activities are also required to be more optimal in managing and applying the mudharabah accounting concept as an opportunity or opportunity to develop business or investment with capital that they do not have so as to create a concept of mutual benefit between the parties involved. concerned. In order to obtain these benefits, it is necessary to understand in detail the procedures and criteria and mudharabah accounting that are in accordance with the rules that have been established. So, the purpose of the discussion of this article is to examine the concept of mudharabah accounting in Islamic economics. The importance of this discussion topic as a reference for humans, especially Muslims to be able to understand matters relating to mudharabah accounting.

Behavioral economics and the nature of neoclassical paradigm

Psychological observations are by now well integrated into economics, especially in the theory of finance, as can also be seen in the Nobel Prize awarded to Thaler. On the contrary, Simon’s attempt to reforge economic theory on the paradigm of bounded rationality failed. Starting from the birth of the neoclassical paradigm, we’ll describe the attempt to give it psychological foundations with a direct measurement of utility, then the axiomatic turn of the paradigm and its first anomalies. We’ll then sum up the debate on rationality, taking place in the group of economists led by Simon, which brought to the rational expectations hypothesis. Finally, we’ll discuss the development of behavioral economics and its progressive acceptance in economic theory. This historical reconstruction allows us to understand the actual hard core of the neoclassical paradigm and the growing need of the paradigm for practical flexibility that determines how to choose arguments, methods and evidence that can be useful to its development, including psychological ones.

Income inequality as a determinant of neonatal mortality in the Americas during 2000–2019: implications for the attainment of Sustainable Development Goal target 3.2

BackgroundThe work of the WHO Commission on the Social Determinants of Health has been fundamental to provide a conceptual framework of the social determinants of health. Based on this framework, this study assesses the relationship of income inequality as a determinant of neonatal mortality in the Americas and relates it to the achievement of the Sustainable Development Goal target 3.2 (reduce neonatal mortality to at least as low as 12 deaths per 1,000 live births). The rationale is to evaluate if income inequality may be considered a social factor that influences neonatal mortality in the Americas.MethodsYearly data from 35 countries in the Americas during 2000–2019 was collected. Data sources include the United Nations Inter-agency Group for Child Mortality Estimation for the neonatal mortality rate (measured as neonatal deaths per 1,000 live births) and the United Nations University World Institute for Development Economics Research for the Gini index (measured in a scale from 0 to 100). This is an ecological study that employs a linear regression model that relates the neonatal mortality rate (dependent variable) to the Gini index (independent variable), while controlling for other factors that influence neonatal mortality. Coefficient estimates and their robust standard errors were obtained using panel data techniques.ResultsA positive relationship between income inequality and neonatal mortality is found in countries in the Americas during the period studied. In particular, the analysis suggests that a unit increase in a country’s Gini index during 2000–2019 is associated with a 0.27 (95% CI [− 0.04, 0.57], P =.09) increase in the neonatal mortality rate.ConclusionThe analysis suggests that income inequality may be positively associated with the neonatal mortality rate in the Americas. Nonetheless, given the modest magnitude of the estimates and Gini values and trends during 2000–2019, the findings suggest a potential limited scope for redistributive policies to support reductions in neonatal mortality in the region. Thus, policies and interventions that address higher coverage and quality of services provided by national health systems and reductions in socio-economic inequalities in health are of utmost importance.

Women's Development Training To Encourage Women's Participation In The Aspect Of Development (Economic, Political, And Social) In The Implementation Of Sustainable Development Goals (SDGs) In Kampung Nelayan Seberang Belawan

Women's Development Training To Encourage Women's Participation In The Aspect Of Development (Economic, Political, And Social) In The Implementation Of Sustainable Development Goals (SDGs) In Kampung Nelayan Seberang Belawan

Can the development of the rural digital economy reduce agricultural carbon emissions? A spatiotemporal empirical study based on China's provinces

Rapid advancement of the rural digital economy has intensified the demand for leveraging digital tools to foster low-carbon and sustainable agricultural practices, garnering widespread academic and bureaucratic attention. Understanding how the rural digital economy influences agricultural carbon emissions is crucial for unlocking emission reduction potential, facilitating a transition towards sustainable energy usage in rural areas, and nurturing green agricultural development. In this study, we employ the entropy method, a spatial Durbin model, and a panel threshold model to assess the impact of the rural digital economy on agricultural carbon emissions across each province in China from 2010 to 2022. Additionally, we delve into the mechanism through which the rural digital economy facilitates agricultural carbon reduction, particularly in terms of “agricultural socialized services”.Our findings reveal several key insights. Firstly, the rural digital economy contributes significantly to reducing agricultural carbon emission intensity. Secondly, there is a non-linear relationship between the rural digital economy and agricultural carbon emissions. With the development of rural digital economy showing a marginal decreasing trend, there is an obvious threshold effect. Thirdly, enhancing agricultural socialized services through the rural digital economy can curb agricultural carbon emissions. Lastly, the carbon reduction effect of the rural digital economy is more significant in more economically developed areas, areas with moderate levels of economic development, and areas with low technological investment; implementation of a “zero growth” policy for fertilizers strengthens this carbon reduction effect. This study sheds light on the mechanisms and effects of agricultural carbon emissions, offering quantitative evidence and theoretical support for the transition towards low-carbon and sustainable agricultural development.

Shadow of Technology: Robotics Development and Regional Economic Inequality

Shadow of Technology: Robotics Development and Regional Economic Inequality