Objective Of Project Research Articles

Reality vs. Expectations in the Implementation of Urban Agricultural Projects—A Polycentric Governance Analysis

Population growth and urbanization are threatening food security. Urban agriculture is therefore a solution for urban food production and distribution. This paper investigates a multi-level governance framework to evaluate how local authorities implement their prescriptions at different levels of decision-making and objectives for urban agricultural projects and their role in building urban resilience. A qualitative assessment based on interviews and fieldwork over two periods in 2023 and 2024 was conducted with stakeholders from different entities in the Normandy Region of France, including the metropolis, the city, and two projects’ presidents. The findings revealed a positive alignment on polycentric governance between different entities in terms of socio-economic integration, climate improvement, and nutritional diversity, all of which were achieved by the cases evaluated. Additionally, local authorities are seeking to achieve urban food self-sufficiency in order to reduce the scale of food imports, thus highlighting a limitation and challenging aspect of this study, given that urban areas are compromised by population density, limited space capacity, and the impermanence of projects. This investigation clearly shows that using this combined systematic approach of interviews and fieldwork provides an in-depth understanding of authorities’ needs and assesses the existence of polycentric governance compliance across multiple units.

Wind Energy Siting Optimization in Fujian Province, China

The geographical distribution and scientific evaluation of wind energy potential are crucial for regional energy planning. Wind energy is a renewable energy that can mitigate climate change. Several open-access World Bank databases and the ESRI (Environmental Systems Research Institute) Global were used to gather and process data through wind energy siting optimization in Fujian Province. This paper uses the fuzzy quantifiers of the multi-criteria decision-making (MCDM) approach in arc geographic information system (ArcGIS Pro) and the analytical hierarchy process (AHP) to handle the associated wind data uncertainties to obtain wind energy technology siting optimization for nine cities in Fujian Province. The converted database options and characteristics used the weighted overlay tool (WOT) to reflect the importance of wind farm project objectives. The sensitivity analysis tested the robustness and resilience of the integrated MCDM design for feasibility or viability. The results revealed that 21.743% of the area of Longyan City is suitable for siting wind energy. Other cities’ suitable areas comprise 14.117%, 12.800%, 5.250%, 4.621%, 4.020%, 4.020%, 3.430%, and 2.300%, respectively (Sanming, Ningde, Quanzhou, Putian, Zhangzhou, Nanping, Xiamen, and Fuzhou cities). Furthermore, a considerable amount of wind power is needed to supply the current primary energy deficit (60.0–84.0%) and satisfy the carbon emission reduction target. Wind farm installation in Fujian province is an opportunity to provide inexhaustible energy, generally affected by generation volume and operational span. Wind power is highly acceptable to local Chinese. Reasonably high understanding and excitement for wind farm investments exist among local authorities. Future research should consider wind data of the identified onshore optimization sites and design wind farms for the respective output power for pessimistic, average, and optimistic scenarios for possible wind farm development. Similarly, the long shoreline of about 1680.0 miles (or 2700.0 km) is a considerable source of offshore wind power prospecting, future research, and energy exploitation and harvesting opportunities.

Sanctions against russian science: Pros and cons

This paper examines the advantages and disadvantages of imposing sanctions on russian science, along with the measures enacted to penalize or restrict it. Proponents of sanctions argue that accountability is essential, and a tangible cost must be imposed for russia’s actions. Sanctions can serve as a signal to end war and aggression, weaken russia’s economic foundation, and exacerbate brain drain, potentially hindering the development of military technologies and reducing russia’s capacity to sustain its aggression. Furthermore, some russian academics openly support the war and should be held accountable for their stance. However, significant opposition to sanctions exists. A prevailing argument, often mirrored in fields like sports and culture, is that science should remain apolitical. Critics contend that sanctions disrupt the free exchange of ideas, penalize innocent individuals, and may harm global scientific progress and human development, given russia’s integral role in international research. Additionally, sanctions are often criticized as costly and inefficient. Despite these debates, a variety of restrictive measures targeting russian science have been implemented. These include funding cuts, suspension of collaborations, termination of joint projects, and exclusion of academics linked to the russian regime. Other measures involve companies closing R&D facilities in russia, restrictions on scientific equipment and reagents, boycotts of russian-organized conferences, and the suspension of international research partnerships. This study synthesizes the arguments for and against sanctions on russian science and provides an analytical discussion to shape a nuanced perspective on this complex issue.

Characteristics and projected changes in maximum daily precipitation across the globe

AbstractResearch indicates a notable rise in extreme precipitation events across the globe in association with a warmed climate. A considerable chunk of such assessments focuses either on global land areas or specific regions, and these have often been reported on an annual basis. However, it is crucial to note that significant monthly variations occur in the precipitation regimes, especially during seasonal transitions. This research evaluates the statistical characteristics of extreme precipitation globally in observations and Coupled Model Intercomparison Project Phase 6 model projections. Our assessment focuses on maximum daily precipitation on an annual, seasonal, and monthly scale globally for both historical and future climate in the 21st century. The historical assessment is conducted based on the 1980–2014 period, whereas the future climate is sectioned into near future (2040–2060) and far future (2070–2090) focusing on two emission scenarios: SSP245 and SSP585. Monthly assessments of maximum daily precipitation indicate an expansion in the probability density curve's width starting from the boreal winter season going into the peak‐to‐late summer season both in observations and model simulations. Transitioning between unimodal and bimodal distributions in density curves indicates the appearance of one more class of grid points that receive higher precipitation; for example, over the land. Despite large inter‐model spread, future projections assessed based on multimodel mean reveal a substantial rise in maximum daily precipitation in a warmer climate. The reported increase in the intensity of maximum daily precipitation compared with historical climate outweighs the decrease by at least a factor of 1.2 in both scenarios, which is, further, more prominent in SSP585. A large decrease and increase in the intensity of extremes indicates that it may become more variable and intense in future. Additionally, land regions, particularly along the intertropical convergence zone and global monsoon domains, are expected to exhibit more pronounced intensification in future, whereas the decline is mostly confined to tropical–subtropical oceanic regions.

Multi-method research in the digital age: A methodological case study of a broad-scale rapid rural appraisal

ABSTRACT This article presents a methodological case conceptualized de novo during the pandemic to identify priorities for investment in rural community development. To achieve project objectives, traditional research approaches were adapted to an entirely virtual setting. The project was a two-stage, mixed-methods rapid appraisal process, with data analyzed using the Community Capitals Framework. First, an online survey was conducted, collecting responses from rural community development practitioners across the U.S. Second, quantitative and qualitative analyses were conducted to identify rural development priority areas. Results were used to design and facilitate virtual dialogue sessions with key informants to inventory assets, challenges, and opportunities in rural communities. Dialogue outcomes contributed to identifying priority areas for national and regional investments in rural development. This article opens dialogue within the field on community-engaged research in the digital age. Lessons learned include challenges of institutional trust, broadband access, and methodological modifications to accommodate virtual settings.

Project leadership and representation: powering purposive social value

PurposeThe paper discusses sustainability, in terms of creating purposive social value, which is increasingly an inherent characteristic of project leading and doing. Consequently, project scholars are currently grappling with what purposive social value creation means to initiation and execution of projects.Design/methodology/approachIn line with recent development we offer reflections on the empowering nature of leadership enabling social value, its underlying forms of power and the dilemmas involved. Our theorizing is based on recent developments in literature on the value of sustainability in the context of projects, combined with literature on project leadership and reflections on the processes of power.FindingsBased on a process ontology, we suggest that leadership for purposive social value creation calls for a rethinking of leadership as an inclusive patterning of actions, making it relational, boundary spanning and socialized in nature and essentially entailing a politics of representation.Research limitations/implicationsThe paper is conceptual, so it has all the limitations of any conceptual paper that innovates a new field of inquiry. It is a not a hypothetic-deductively structured paper but is abductive in nature, combining disparate literary studies in a conceptual framework for further inquiry.Practical implicationsThe legitimacy of leadership for purposive social value does not reside in the singular formal position of a project leader but in its broader vesting and representation.Social implicationsThe paper calls for a rethinking of the nature of project leadership power relations, processes oriented toward the achievement of purposive social value and representation of interests.Originality/valueThis paper fulfills an identified need to study how purposive social value can be enabled by socialized and distributed project leadership that represents a broad-based and coherent conception of interests.

Follow the user: Taking advantage of Internet users as methodological resources

In this article, I introduce a novel epistemological approach meant to re-adapt the digital methods paradigm to a fast changing digital landscape. This change was mostly brought about by the transformation of Web 2.0 into a platformized social media environment and the advent of the post-API and platform surveillance era. This approach is premised on the idea of considering the Internet user as a source of methods, rather than an object of study. To this purpose I suggest to ‘ follow the user’, meaning to take advantage of the natively digital methods that Internet users employ to gather, organise, manage and create their own digital data throughout their everyday digital practices. In order to illustrate my approach, I show how to practically follow the users in each of the key phases of a digital research project within social media platforms: keywords selection, data collection, data sampling, data analysis. In doing so, I mean to contribute to an emerging strand of research within the digital methods tradition which is trying to: (a) update digital methods, given the continuous mutations of the digital landscape; (b) seek a more organic and systematic integration of digital and qualitative methods; (c) go beyond the classical quali-quanti dichotomy.

Exploring the Relationship Between Upper Ocean States and the Falling Ice Radiative Effects using ECCO Product and Global Climate Models

Abstract This study seeks to explore the relationship between upper ocean current (UOC) anomalies (above 200 meters) and surface wind stress (TAU), focusing on the influence of falling ice (snow) radiative effects (FIREs) over the tropical and subtropical Pacific regions. To achieve this, we conducted sensitivity experiments with the CESM1-CAM5 model, using the Coupled Model Intercomparison Project phase 5 (CMIP5) historical run setting, with FIREs turned off (NOS) and on (SON). The monthly ocean current and temperature of the ocean reanalysis from the NASA Estimating the Circulation and Climate of the Ocean (ECCO) project, which assimilates satellite and in situ measurements, serve as a reference for this study. The spatial patterns of the horizontal UOC anomaly (UOCA) differences between the NOS and SON experiments show a strong correlation with the TAU patterns across the studied domain. When compared to the experiments with NOS, the experiments with SON demonstrate an improvement in the annual mean UOC. The improvement in UOC can be attributed to the enhancements in TAU, specifically in the trade-wind regions. The enhancements in TAU play a significant role in influencing the UOCA patterns and contribute to the overall improvement observed in the experiments with SON. In SON, the average absolute bias of simulated UOCA over the study area is reduced by up to 30% compared to NOS against ECCO. 
Although biases in UOC are present over the southern and northern flanks of the equator in SON, the improvements in annual mean ocean currents are closely related to enhancements in TAU driven by the inclusion of FIREs. Notably, stronger ocean current magnitudes correspond to more significant changes in TAU due to Coriolis forces. When evaluating the ensemble mean absolute biases of UOC from the CMIP5 models, similarities to NOS, however, are limited over the South Pacific region.

Anthromes and forest carbon responses to global change

Societal Impact StatementForest ecosystems absorb and store about 25% of global carbon dioxide emissions annually and are increasingly shaped by human land use and management. Climate change interacts with land use and forest dynamics to influence observed carbon stocks and the strength of the land carbon sink. We show that climate change effects on modeled forest land carbon stocks are strongest in tropical wildlands that have limited human influence. Global forest carbon stocks and carbon sink strength may decline as climate change and anthropogenic influences intensify, with wildland tropical forests, especially in Amazonia, likely being especially vulnerable.Summary Human effects on ecosystems date back thousands of years, and anthropogenic biomes—anthromes—broadly incorporate the effects of human population density and land use on ecosystems. Forests are integral to the global carbon cycle, containing large biomass carbon stocks, yet their responses to land use and climate change are uncertain but critical to informing climate change mitigation strategies, ecosystem management, and Earth system modeling. Using an anthromes perspective and the site locations from the Global Forest Carbon (ForC) Database, we compare intensively used, cultured, and wildland forest lands in tropical and extratropical regions. We summarize recent past (1900‐present) patterns of land use intensification, and we use a feedback analysis of Earth system models from the Coupled Model Intercomparison Project Phase 6 to estimate the sensitivity of forest carbon stocks to CO2 and temperature change for different anthromes among regions. Modeled global forest carbon stock responses are positive for CO2 increase but neutral to negative for temperature increase. Across anthromes (intensively used, cultured, and wildland forest areas), modeled forest carbon stock responses of temperate and boreal forests are less variable than those of tropical forests. Tropical wildland forest areas appear especially sensitive to CO2 and temperature change, with the negative temperature response highlighting the potential vulnerability of the globally significant carbon stock in tropical forests. The net effect of anthropogenic activities—including land‐use intensification and environmental change and their interactions with natural forest dynamics—will shape future forest carbon stock changes. These interactive effects will likely be strongest in tropical wildlands.

Digital Twin in the Design and Dynamic Assessment of Energy Performance of Multi-Family Buildings

The article explores the potential of Digital Twin (DT) technology in the design and dynamic assessment of the energy performance of multi-family buildings. Traditional approaches to building energy assessment provide static data that do not account for changing operational conditions and lack continuous energy consumption-monitoring capabilities. The use of Digital Twin enables monitoring and analyzing of the building’s energy parameters at every stage of its life cycle. The article presents the application of DT technology for assessing energy performance at the conceptual stage and in the early phases of design. These parameters must meet legal requirements. Validation conducted on four multi-family buildings demonstrated high accuracy, with the average difference between predicted and actual energy performance (EP) values below 3.5%. Thanks to the DT model, it is possible to determine energy parameters already at the conceptual stage, which helps avoid costly changes in later project phases. Early determination of these parameters also allows for accurate estimation of design and investment costs. Tests of the proposed solution were conducted on several multi-family buildings, comparing preliminary data with final results. The research results show that DT technology allows for precise planning of energy performance at the conceptual and preliminary design stages. This reduces operational costs, increases energy efficiency, and better adapts buildings to changing technological and legal conditions.

Evaluation of the Duration of Albedo Measurement Campaigns

Bifacial PV modules are increasingly used in commercial solar PV projects. With the use of the solar energy available at the module’s rear surface, the correct estimation of the albedo during the pre-construction phase of commercial projects becomes more important. The intention of the study is to determine the impact of the duration of a short-term albedo measurement campaign to reduce the uncertainty with respect to satellite albedo, thus providing a ‘beneficial campaign’. Albedo measurements from seven sites in the USA have been used to estimate the impact of 1-day and 7-day measurements (short-term measurements) on the simulated annual electrical energy production. Simulations based on monthly albedo data obtained from a recognised satellite provider have been compared with simulations where the albedo has been corrected based on short-term measurements. The study found that a 1-day campaign is often not beneficial, causing an increase in uncertainty with respect to satellite albedo. Measurement campaigns in winter do not generally reduce the error of the annual electrical energy production simulation, however, a 7-day campaign undertaken in summer appears to often reduce the uncertainty. It should be noted that the climate conditions, ground vegetation and measurement setup of the analysed seven sites in the USA may not be representative for other sites in the rest of the world.

IMPLEMENTASI SISTEM TANGGAP DARURAT KEBAKARAN PADA GEDUNG UTAMA DI INDUSTRI PUPUK

A fire emergency response system is a series of procedures that must be carried out when a fire occurs to protect the safety of building occupants. The main objective of this final project is to ensure that the fire emergency response system in the main building of in the fertilizer industry. can function well when a fire occurs and can protect the safety of all main building employees. This research uses a qualitative research method which uses two types of data, namely primary data with in-depth interviews and observations, and secondary data from document reviews, company archives and literature studies. The results and discussion based on variables related to the implementation of the fire emergency response system can be concluded that the informants implemented the fire emergency response system procedures well and in accordance with established regulations. This is proven based on observations that in the main building of PT.X already has a fire emergency response system, such as a fire detection and alarm system, fire extinguishing system, evacuation routes, gathering points and other supporting equipment. Apart from that, there are also evacuation procedures, training and simulations, as well as maintenance of a fire emergency response system that is complete and in accordance with established regulations.

The Category of Graphs

The category of graphs and the mappings between them is considered. The monomorphisms and epimorphisms are characterized. Reflective and coreflective subcategories are identified and terminal, initial, projective, and injective objects are characterized. Parallels with the category of topological spaces are discussed.

Diagnosing the factors that contribute to the intermodel spread of climate feedback in CMIP6

Abstract The global surface temperature climate feedback parameter (λ) varies significantly across climate models, and its real-world value remains uncertain. Studies have found that sea surface temperature (SST) response pattern and atmospheric model physics can each affect the climate feedback parameter in historical and idealized warming simulations. In this study, we design and analyze a series of targeted atmospheric global climate model (AGCM) experiments to quantify how much the SST (both the warming pattern and the base climatology) and atmospheric model physics contributes to the intermodel spread of the climate feedback parameter and cloud feedback in in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We use three AGCMs, HiRAM, AM2.5, and AM4, developed in the Geophysical Fluid Dynamics Laboratory (GFDL), which span a wide range of feedbacks in response to uniform surface warming. The three GFDL models indicate that historical patterns of SST change systematically alter the λ, supporting the hypothesized role for a “pattern effect” in historical climate sensitivity. However, we found that forcing one AGCM with the different CO2-induced SST warming patterns or model SST climatology from a suite of climate models from CMIP6 can only reproduce ~10% the intermodel spread of CO2-induced λ, while the atmospheric model used determines the magnitude of λ (~45%). This underscores the role of atmospheric model physics in altering λ, particularly the cloud-related schemes. In addition, we demonstrate that the nonlinear interaction between SST and AGCM has a non-negligible role in affecting λ.

StockSight: Insightful Predictions for Strategic Investments

Analysts have been examining different strategies to viably anticipate stock advertise costs. Valuable expectation frameworks give dealers with way better experiences into future patterns, profiting speculators by analyzing future showcase conditions. This project's primary objective is to realize superior exactness in forecasts compared to past models utilizing distinctive machine learning calculations. In this work, we employ three machine learning algorithms: Random Forest, LSTM, and Decision Tree Regressor. We also use four evaluation metrics: R Squared Error, MAE, MSE, and RMSE, to predict the stock prices of JPX Tokyo Stock Exchange and ITC Limited, and to compare their accuracy. The JPX Tokyo Stock Exchange and ITC Limited stock price data were downloaded from the Kaggle website. The dataset attributes include Date, Open, High, Low, Close, Adj Close, and Volume. The full source code of the project was written in Python. It is hypothesized that the LSTM model provides more accurate predictions than the Random Forest and Decision Tree Regressor for Tokyo stock data, while Random Forest offers more accurate predictions than LSTM and Decision Tree Regressor for ITC Limited stock data.

Empowering emerging evaluators in evaluation project phases: Perspectives and recommendations

Background: Engaging emerging evaluators (EEs) in evaluation projects offers substantial benefits for both evaluators and organisations; however, effective guidance on empowering them is often limited.Objectives: This article provides actionable recommendations for empowering EEs across all phases of evaluation projects, drawing primarily on insights from EEs and their mentors. Leveraging the Eval4Action campaign’s standards for meaningful youth engagement, this article aims to enrich EEs’ learning experiences, foster their professional growth, and enhance evaluation quality and impact.Method: A qualitative and participatory approach was used, incorporating the author’s mentorship experiences, discussions with EEs and mentors, and consultations with established programmes such as the South African Monitoring and Evaluation Association’s (SAMEA) EE initiative and the EvalYouth Global Network. A comprehensive literature review on youth engagement and partnerships further informs this study.Results: Findings indicate that effective EE engagement relies on well-defined roles, open communication, and genuine appreciation of EEs’ contributions. Despite these needs, many EEs reported being limited to data collection roles, which restricts their opportunities for growth and limits their developmental impact within evaluations.Conclusion: Adopting these recommendations can foster a culture of empowerment and inclusion, enhancing both the quality and effectiveness of evaluation projects.Contribution: This study underscores the importance of engaging EEs in meaningful roles throughout projects, advancing inclusivity and effectiveness in evaluations. Empowering EEs leads to more comprehensive assessments, providing deeper insights for policy and programme evaluation.

Challenges in AI Implementation: Perspectives from Practice and Research

Artificial Intelligence (AI) has become an inevitable topic for organizations across various sectors and sizes, offering promising applications as technological accessibility continues to expand. Despite its potential, practical implementation of AI-based Systems remains difficult with particular challenges tied to specific organizational contexts. Often companies invest heavily in AI development but encounter problems such as failure to achieve market readiness of the prototype or the systems struggling to deliver expected benefits. These setbacks often stem from flawed implementation strategies, excessive reliance on technology, or inadequate integration into existing organizational frameworks. Therefore, this paper addresses these challenges encountered at different phases of AI implementation projects. To this end, we initially conduct a Rapid Structured Literature Review (Armitage and Keeble-Allen, 2008), examining the literature on AI implementation cases and associated scholarly reviews. Extending the initial analysis, we experiment with AI driven document analysis as a means to integrate findings of a greater amount of publications into the review. The literature review is subsequently complemented by insights from our own consultancy experiences from the field of AI consultancy. The paper gives an overview of the most salient challenges in AI implementation projects and points out some approaches to mitigate those challenges. From a methodological standpoint it shows that AI driven reviews can yield similar results as conventional reviews, but may lack some explanatory depth. We find that a combination of manual and automated approaches tends to be the most effective strategy.

Integrating Stakeholder Priorities for the Optimization of Care of the Preterm Infant in a Safety-Net NICU.

This study aimed to engage clinical and community stakeholders to create a prioritization matrix of interventions to reduce neonatal brain injury and improve neurodevelopmental outcomes. We collaborated with our community partner to establish a Lived Experience Advisory Group (LEAG). Faculty performed a literature review to identify neonatal neuroprotective interventions; additional priorities from the LEAG were also included. Project leaders scored and presented intervention bundles for impact. Stakeholder priorities for value/feasibility were obtained via questionnaire. Mean values were incorporated into a prioritization matrix. Themes from discussions with LEAG members included the role of the neonatal intensive care unit (NICU) parent, the experience of trauma, shared decision-making, communication and trust with the health care team, and bias in medical care. Five interventions were placed in the highest priority quadrant of the prioritization matrix: thermoregulation, maternal antepartum steroids, delayed cord clamping, optimized oxygen delivery, and optimized glucose regulation. It is feasible to incorporate community and parent values into clinically oriented neuroprotective intervention bundles for preterm infants. This clinical-community collaboration is vital to ensure that our project objectives meet the needs and priorities of the population it is intended to serve. · Including input from parent advisory groups supports interventions that meet the needs of families.. · Collaboration between clinical and community stakeholders can improve neuroprotective and neurodevelopmental strategies.. · Incorporation of clinical interventions into a prioritization matrix improves resource utilization.. · Five perinatal interventions were ranked highly for impact and feasibility in the prioritization matrix..

An Analysis of Knowledge Management Changes Through Artificial Intelligence with Probst's Model

In recent years, the integration of Artificial Intelligence (AI) into Knowledge Management (KM) has led to transformational changes. These changes have significantly enhanced traditional KM processes. To identify how AI technologies improve and reshape knowledge processes, this study conducted a systematic literature review. The review identified AI technologies suitable for each of Probst's building blocks, which outline the eight central KM processes. The research reveals a wide range of AI technologies, including machine learning, natural language processing and chatbots such as ChatGPT. These technologies can be applied in different domains and introduce innovative approaches to improve KM processes. Based on the AI technologies analysed, this study proposes a four-stage model to support the documentation and application of best practices and lessons learned. The model is designed to enhance the knowledge development process and aims to document and secure key project developments in the long term. A further objective was to analyse which KM process is most affected by chatbots. The findings indicate that chatbots have the potential to transform the use of knowledge in organisations. They act as facilitators by breaking down existing barriers, foster an open culture of knowledge sharing, streamline workflows and increase the accessibility of knowledge. The study also examines the broader changes that AI will bring to KM and forecasts the sixth generation of KM. It draws on Bencsik's (2021) evolutionary and revolutionary perspectives that specifically forecast this next generation. The study shows that AI not only enhances existing KM processes but also has the potential to fundamentally disrupt traditional methods and approaches. These findings underline the need for future research to explore the effective integration and scalability of AI technologies in real-world KM environments. This will help ensure that their long term impact and potential benefits are fully understood across different industries and organisational contexts.

Assessment of extreme climate stress across China’s maize harvest region in CMIP6 simulations

Climate change is expected to increase the frequency and severity of climate extremes, which will negatively impact crop production. As one of the main food and feed crops, maize is also vulnerable to extreme climate events. In order to accurately and comprehensively assess the future climate risk to maize, it is urgent to project and evaluate the stress of extreme climate related maize production under future climate scenarios. In this study, we comprehensively evaluated the spatio-temporal changes in the frequency and intensity of six extreme climate indices (ECIs) across China’s maize harvest region by using a multi-model ensemble method, and examined the capability of the Coupled Model Intercomparison Project Phase 6 (CMIP6) to capture these variations. We found that the Independence Weight Mean (IWM) ensemble results calculated by multiple Global Climate Models (GCMs) with bias correction could better reproduce each ECI. The results indicated that heat stress for maize showed consistent increase trends under four future climate scenarios in the 21st century. The intensity and frequency of the three extreme temperature indices in 2080s were significantly higher than these in 2040s, and in the high emission scenario were significantly higher than these in the low emission scenario. The three extreme precipitation indices changed slightly in the future, but the spatial changes were more significant. Therefore, with the uncertainty of climate change and the differences of climate characteristics in different regions, the optimization of specific management measures should be considered in combination with the specific conditions of future local climate change.