Historical Trends Research Articles

Scenario-Based Modeling of Agricultural Nitrous Oxide Emissions in China

Agricultural land in China represents a major source of nitrous oxide (N2O) emissions, and as population growth and technological advancements drive agricultural intensification, these emissions are projected to increase. A thorough understanding of historical trends and future dynamics of these emissions is critical for formulating effective mitigation strategies and advancing progress toward the Sustainable Development Goals. This study quantifies N2O emissions across 31 provinces in China from 2000 to 2021, employing the IPCC coefficient method alongside China’s provincial greenhouse gas inventory guidelines. The spatiotemporal evolution of emission intensities was examined, with the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model employed to assess the influence of population, technological development, economic growth, and energy structure. The findings confirm that agricultural land remains the primary source of N2O emissions, with significantly higher levels observed in eastern coastal regions compared to western inland areas. Implementing targeted mitigation strategies, such as enhanced agricultural- and manure-management practices and region-specific interventions, is imperative to effectively curb the rising emission trends.

Seasonal and annual trends in reference evapotranspiration and prediction using machine learning models across seven climatic zones of Bangladesh

ABSTRACT The primary aim of this investigation is to examine the historical (1989–2020) and future trends and magnitude of Reference Evapotranspiration (ET0) in terms of spatiotemporal measures, considering its significance as a hydro-meteorological parameter influenced by changing climate. The FAO-56 Penman-Monteith method is employed to analyze ET0, while the Modified Mann Kendall test is utilized to assess trends and Sen’s Slope Estimator is used for magnitude analysis. The future prediction is conducted using Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF) models. Results show an increasing ET0 trend annually in the southeastern and northeastern zones, while decreased values are observed in other regions, particularly in the northwest (0.83 mm). Sen’s slope estimator reveals distinct fluctuations in ET0, with notable disparities in the Southeastern, Northeastern, Southwestern, and South-Central zones. Notably, Chattogram and Sitakunda experience an ET0 magnitude of 0.02 mm/Year, while other zones show a magnitude of 0.01 mm/Year. SVM outperformed other models, predicting rising ET0 in various seasons. These findings offer insights for optimizing irrigation systems and sustainable water management under changing climatic conditions.

Urban expansion and agricultural land loss: a GIS-Based analysis and policy implications in Hawassa city, Ethiopia

This study investigated the historical and future trends of urban expansion and its subsequent impact on agricultural land-use in Hawassa city, Ethiopia. A time-series of remote-sensing imageries from Landsat Thematic Mapper for the years 1984, 1990, 2000, and 2010 and Operational Land Imager for 2021 were used to extract the LULC information from the study area. Seven major land-cover classes’ waterbody, built-up, agricultural land, wetland, grassland, woody vegetation, and agroforestry were identified with visual image interpretation along with supervised image classification techniques using the maximum-likelihood algorithm for the study years. The urban and agricultural lands were then extracted from the original LULC data to quantify the extent, rates, and number of area conversion between the two. The Land Change Modeler module of TerrSet software was used to predict the spatial extents of built-up and agricultural lands in 2030 and 2050. The results showed that there have been significant changes between the LULC types in Hawassa city within the past 37 years, from which built-up and agricultural land have shown the most prevalent changes. It showed that built-up land has increased from 584.73 ha in 1,984–3,939.03 ha in 2021, representing a 573.65% increase at an annual growth rate of 15.50%. However, agricultural land decreased from 8,324.64 ha to 3,595.68 ha in the respective years, with a 56.81% decrease at a rate of −1.54% each year. A total of 3,148.74 ha (37.82%) of agricultural land was converted into built-up land within the past 37 years (85.10 ha per year, a rate of 1.02%. The built-up land is projected to increase to 5,009.85 ha and 6,794.73 ha from 2021 to 2030 and 2050, with annual growth rates of 3.02% and 2.50%, respectively. In the same years, agricultural land will decrease to 2,849.58 ha and 2033.46 ha by 2.31% and 1.50% annually, respectively, from which 64.76 ha (1.80%) and 48.41 ha (1.35%) will be converted into built-up land, respectively. Future planning and development in the city should consider the rapid increase in built-up land toward agricultural land areas and develop appropriate adaptation mechanisms for the local community, which is highly dependent on agriculture.

Evaluation of Effectiveness and Multi-Scenario Analysis of Land Use Development Strategies and Ecological Protection Redlines on Carbon Storage in the Great Bay Area of China Using the PLUS-InVEST-PSM Model

Land use change is a key factor affecting the carbon storage of terrestrial ecosystems. Most studies focus on formulating different land development strategies to mitigate the adverse impacts of land development, while fewer discuss the effectiveness of these strategies. In the context of varying socio-economic development and limited budgets for ecological conservation, evaluating effectiveness is essential for selecting the most suitable land development strategy. This research proposed a Patch-Generating Land Use Simulation-Integrated Valuation of Ecosystem Services and Tradeoffs–Propensity Score Matching (PLUS-InVEST-PSM) model to evaluate the effectiveness of different land use development strategies in the Greater Bay Area of China as a case study. Specifically, this study analyzed the historical land use changes from 2000 to 2020 and mapped the multi-scenario patterns of land use and carbon storage with the PLUS and the InVEST models from 2030 to 2050. Then, this study employed the PSM model, along with a series of criteria (i.e., similar ecological backgrounds and parallel historical trends), to evaluate the effectiveness of the ecological development strategy and ecological protection redlines on carbon storage compared with the natural development strategy. The results indicate that the ecological development strategy and the ecological protection redline can prevent the decline in carbon storage. However, in the ecological development strategy, implementing the ecological redline policy may hinder the growth of carbon storage within the ecological redline area. Compared with the PLUS-InVEST-PSM model, the comparison between the subregions could underestimate the efficiencies of evaluation, partly due to underestimating the negative impact of urban development on carbon storage. These findings will help governments develop comprehensive and systematic land use policies to achieve carbon peaking and carbon neutrality goals. Also, the approach would help to further explore the broader impacts of land use development strategies on the overall regional ecological environment, such as biodiversity and ecosystem services.

Understanding the Historical Trend of Final Energy Intensity of GDP During Economic Transitions: The Case of Portugal (1960–2014)

Understanding the Historical Trend of Final Energy Intensity of GDP During Economic Transitions: The Case of Portugal (1960–2014)

Drought Dynamics in Sub-Saharan Africa: Impacts and Adaptation Strategies

The escalation in both frequency and severity of drought events has significantly amplified the vulnerability of numerous countries, particularly in developing ones, imposing substantial economic, environmental, and social pressures. This article presents a systematic review of drought occurrences in Sub-Saharan Africa (SSA), examining historical trends, current impacts, and projected future implications. Through this comprehensive assessment, a clear trend of intensifying drought phenomena emerges across SSA, leading to crop failures, drying of water sources, loss of pasture, food shortages, and an increase in food prices. This review also highlights the concerning potential for worsening conditions in certain regions, resulting in consequences such as migration, food insecurity, malnutrition, family disintegration, crop losses, and increased disease prevalence, notably HIV/AIDS. This study further reveals that current adaptation measures by governments and NGOs should be improved to effectively adapt to the diverse impacts of drought, and it contributes to a deeper understanding of drought dynamics in Sub-Saharan Africa and assesses its critical impacts on food security and social well-being. It also evaluates adaptation measures across different countries, highlighting their strengths and weaknesses and enabling quick identification of areas for improvement. Additionally, it informs resilience-building efforts in vulnerable communities.

Learning A-Share Stock Recommendation from Stock Graph and Historical Price Simultaneously

The Chinese stock market, marked by rapid growth and significant volatility, presents unique challenges for investors and analysts. A-share stocks, traded on the Shanghai and Shenzhen exchanges, are crucial to China’s financial system and offer opportunities for both domestic and international investors. Accurate stock recommendation tools are vital for informed decision making, especially given the ongoing regulatory changes and economic reforms in China. Current stock recommendation methods often fall short, as they typically fail to capture the complex inter-company relationships and rely heavily on financial reports, neglecting the potential of unlabeled data and historical price trends. In response, we propose a novel approach that combines graph-based structures with historical price data to develop self-learned stock embeddings for A-share recommendations. Our method leverages self-supervised learning, bypassing the need for human-generated labels and autonomously uncovering latent relationships and patterns within the data. This dual-input strategy enhances the understanding of market dynamics, leading to more accurate stock predictions. Our contributions include a novel framework for label-free stock recommendations with modeling stock connections and pricing information, and empirical evidence demonstrating the robustness and adaptability of our approach in the volatile Chinese stock market.

Investigating Effective Reconnaissance Drought Index Ability to Reproduce Drought Signature over the Massili Basin (Burkina Faso)

Drought is a significant natural hazard particularly in arid and semi-arid regions where water resources management is already challenging. Burkina Faso, a landlocked country located in the Sahel region, is highly vulnerable to drought due to its arid climate. The country has experienced recurrent droughts since the 1970s, with significant impacts on its population and economy. To develop effective drought mitigation strategies, a comprehensive understanding of drought characteristics is required. This study investigates historical long-term drought trends in the Massili basin located in central Burkina Faso. For this purpose, drought features has been analyzed based on the Effective Reconnaissance Drought Index (eRDI) at various months of accumulation. To calculate the Effective Reconnaissance Drought Index for the Massili Basin, monthly precipitation (Prct), minimum temperature (Tmin), and maximum temperature (Tmax) data spanning from 1960 to 2021 were obtained from the National Meteorological Agency of Burkina Faso. The Potential evapotranspiration (ETP) was estimated using the Hargreaves method. Our findings indicate that under eRDI-3, 1964 (1.86), 2020 (1.53), and 2021 (0.63) are the wettest years, while 1963 (-0.65) and 1998 (-0.76) are the driest. Under eRDI-12, a significant portion of the values falls within the range of -0.14 to 0.03. In the case of eRDI-24, a substantial number of the values cluster between -0.08 and 0.08. This distribution highlights near-normal drought conditions (-0.99 to 0.99) as the most frequent occurrence within the watershed. The desertification of the Sahel area has been a topic of discussion for decades. However, these findings of this study reinforce the prevailing belief in a partial re-greening of the Sahel region.

Using Horizon Scanning to Build Policy Resilience: Case of Waste Crime

ABSTRACTWaste crime is a pressing concern for the waste and resource industry as it is undermining investment, growth and jobs within the industry and threatening the natural environment. However, there is little knowledge of the scale of the problem, the types of criminality and motivations involved, and the precise nature of crime. Environmental regulators are building foresight capabilities to better understand the effect of current and future changes in markets, in technology and in the legislative environment on waste crime and associated behaviours. At the heart of this paper is the question: how can horizon scanning be adopted by environmental regulators to shape decision processes and build resilience to waste crime? We report our efforts to build a toolkit and guidance for conducting horizon scanning, aimed at supporting environmental regulators, investigators and intelligence analysts to build an understanding of—and interpretation of the consequences of—behavioural, market, technological and pollution trends in the waste sector. A review of the academic and grey literature provided insights to organisational approaches and design principles for public sector horizon scanning. Outputs guided discussion at a stakeholder workshop with waste regulators, criminal intelligence and industry professionals to explore institutional challenges and to agree broad design principles for a horizon scanning process. The toolkit supports environmental regulators in applying horizon scanning to policy and wider operational and delivery‐focused challenges; learning how to: (1) spot weak signals and emerging trends quickly, (2) examine the evidence around potential threats and opportunities for the future, and (3) take action on strategically important issues to minimise the impact of crime on the environment, society and business. The paper sets out further research needed to integrate horizon scanning with data analytics (e.g., predictive and hotspot analyses) to challenge assumptions about the patterns of change, based largely on historical trends, and to better manage these so there is greater adaptability to current and future trends.

A Phylogenetic Assessment of HIV-1 Transmission Trends among People Who Inject Drugs from Coastal and Nairobi, Kenya

ABSTRACT Background Although recent modeling suggests needle-syringe programs have reduced parenteral HIV transmission among people who inject drugs (PWID) in Kenya, the prevalence in this population remains high (~14-20%, compared to ~4% in the larger population). Reducing transmission or acquisition requires understanding historic and modern transmission trends, but the relationship between the PWID HIV-1 sub-epidemic and the general epidemic in Kenya is not well understood. Methods We incorporated 303 new (2018-2021) HIV-1 pol sequences from PWID and their sexual and injecting partners with 2,666 previously published Kenyan HIV-1 sequences to quantify relative rates and direction of HIV-1 transmissions involving PWID from the coast and Nairobi regions of Kenya. We used genetic similarity cluster analysis (thresholds: patristic distance <0.045 & <0.015) and maximum likelihood and Bayesian ancestral state reconstruction to estimate transmission histories at the population group (female sex workers, men who have sex with men, PWID, or general population) and regional (coast or Nairobi) levels. Results Of 1,081 participants living with HIV-1, 274 (25%) were not virally suppressed and 303 (28%) had sequences available. Of new sequences from PWID, 58% were in phylogenetic clusters at distance threshold <0.045. Only 21% of clusters containing sequences from PWID included a second PWID sequence. Sequences from PWID were similarly likely to cluster with sequences from female sex workers, men who have sex with men, and the general population. Ancestral state reconstruction suggested transmission to PWID from other populations was more common than from PWID to other populations. Conclusion This study expands our understanding of the HIV-1 sub-epidemic among PWID in Kenya by incorporating 4-times more HIV-1 sequences from this population than prior studies. Despite recruiting many PWID from local sexual and injecting networks, we found low levels of linked transmission in this population. This may suggest lower relative levels of parenteral transmission in recent years and supports maintaining needle-syringe programs among PWID while also strengthening interventions to reduce HIV-1 sexual acquisition and transmission for this population.

Changing climate risks for high-value tree fruit production across the United States

Abstract Climate change poses growing risks to global agriculture including perennial tree fruit such as apples that hold important nutritional, cultural, and economic value. This study quantifies historical trends in climate metrics affecting apple growth, production, and quality, which remain understudied. Utilizing the high-resolution gridMET dataset, we analyzed trends (1979-2022) in several key metrics across the U.S. – cold degree days, chill portions, last day of spring frost, growing degree days (GDD), extreme heat days (daily maximum temperature >34°C), and warm nights (daily minimum temperatures >15°C). We found significant trends across large parts of the U.S. in all metrics, with the spatial patterns consistent with pronounced warming across the western states in summer and winter. Yakima County, WA, Kent County, MI, Wayne County, NY—leading apple-producers—showed significant decreasing trends in cold degree days and increasing trends in GDD and warm fall nights. Yakima county, with over 48,870 acres of apple orchards, showed significant changes in five of the six metrics - earlier last day of spring frost, fewer cold degree days, increasing GDD over the overall growth period, and more extreme heat days and warm nights. These trends could negatively affect apple production by reducing the dormancy period, altering bloom timing, increasing sunburn risk, and diminishing apple appearance and quality. Large parts of the U.S. experience detrimental trends in multiple metrics simultaneously that indicate the potential for compounding negative impacts on the production and quality of apples and other tree fruit, emphasizing the need for developing and adopting adaptation strategies.

Development of an approach to forecast future takeaway outlet growth around schools and population exposure to takeaways in England

BackgroundNeighbourhood exposure to takeaways can contribute negatively to diet and diet-related health outcomes. Urban planners within local authorities (LAs) in England can modify takeaway exposure through denying planning permission to new outlets in management zones around schools. LAs sometimes refer to these as takeaway “exclusion zones”. Understanding the long-term impacts of this intervention on the takeaway retail environment and health, an important policy question, requires methods to forecast future takeaway growth and subsequent population-level exposure to takeaways. In this paper we describe a novel two-stage method to achieve this.MethodsWe used historic data on locations of takeaways and a time-series auto-regressive integrated moving average (ARIMA) model, to forecast numbers of outlets within management zones to 2031, based on historical trends, in six LAs with different urban/rural characteristics across England. Forecast performance was evaluated based on root mean squared error (RMSE) and mean absolute scaled error (MASE) scores in time-series cross-validation. Using travel-to-work data from the 2011 UK census, we then translated these forecasts of the number of takeaways within management zones into population-level exposures across home, work and commuting domains.ResultsOur ARIMA models outperformed exponential smoothing equivalents according to RMSE and MASE. The model was able to forecast growth in the count of takeaways up to 2031 across all six LAs, with variable growth rates by RUC (min–max: 39.4-79.3%). Manchester (classified as a non-London urban with major conurbation LA) exhibited the highest forecast growth rate (79.3%, 95% CI 61.6, 96.9) and estimated population-level takeaway exposure within management zones, increasing by 65.5 outlets per capita to 148.2 (95% CI 133.6, 162.7) outlets. Overall, urban (vs. rural) LAs were forecast stronger growth and higher population exposures.ConclusionsOur two-stage forecasting approach provides a novel way to estimate long-term future takeaway growth and population-level takeaway exposure. While Manchester exhibited the strongest growth, all six LAs were forecast marked growth that might be considered a risk to public health. Our methods can be used to model future growth in other types of retail outlets and in other areas.

Contraceptive Change and Fertility Transition

AbstractOver the past half‐century, most countries have made progress through their demographic transitions with continuing declines in mortality and fertility. The major driver of fertility decline has been the adoption of contraception by women of reproductive age who increasingly desire smaller families. This paper documents the massive changes in contraceptive behavior that have occurred since 1970 at the global and regional levels and examines contraceptive use differentials by marital status and method. To understand the proximate causes of the rise in contraceptive use, we document the changes in the potential demand for contraception (among women who want to space or limit their childbearing) and the degree to which this potential demand is satisfied by the actual practice of contraception. The paper concludes with a confirmation of the strong inverse relationship between contraceptive use and fertility. The main sources of data for these analyses are comprehensive datasets with country, regional, and global estimates of historical trends in fertility and contraceptive behavior maintained by the Population Division of the United Nations.

Advanced Short-Term Load Forecasting with XGBoost-RF Feature Selection and CNN-GRU

Accurate and efficient short-term load forecasting (STLF) is essential for optimizing power system operations. This study proposes a novel hybrid forecasting model that integrates XGBoost-RF feature selection with a CNN-GRU neural network to enhance prediction performance while reducing model complexity. The XGBoost-RF approach is first applied to select the most predictive features from historical load data, weather conditions, and time-based variables. A convolutional neural network (CNN) is then employed to extract spatial features, while a gated recurrent unit (GRU) captures temporal dependencies for load forecasting. By leveraging a dual-channel structure that combines long- and short-term historical load trends, the proposed model significantly mitigates cumulative errors from recursive predictions. Experimental results demonstrate that the model achieves superior performance with an average root mean square error (RMSE) of 53.29 and mean absolute percentage error (MAPE) of 3.56% on the test set. Compared to traditional models, the prediction accuracy improves by 28.140% to 110.146%. Additionally, the model exhibits strong robustness across different climatic conditions. This research validates the efficacy of integrating XGBoost-RF feature selection with CNN-GRU for STLF, offering reliable decision support for power system management.

Decarbonizing the global steel industry in a resource-constrained future-a systems perspective.

Decarbonizing the global steel industry hinges on three key limited resources: geological carbon storage, zero-emission electricity and end-of-life scrap. Existing system analysis calls for an accelerated expansion of the supply of these resources to meet the assumed ever-increasing steel demand. In this study, we propose a different view on how to decarbonize the global steel industry, based on the principle that resource supply can only expand in line with historical trends and actual construction plans. Our analysis shows that global steel production cannot grow any further within a Paris-compatible carbon budget, resulting in a shortfall of approximately 30% against 2050 demand. This trajectory involves the phasing out of blast furnaces, along with strong growth in scrap recycling and hydrogen-based production. These findings highlight critical yet often overlooked challenges: (i) reducing excess demand while providing essential services, (ii) producing high-grade steel through upcycling scrap, and (iii) ensuring an equitable distribution of limited production across the globe. These perspectives contrast with those of the current agenda, which largely emphasizes the need to invest in new production technologies. Grounded in a physical basis, this analysis offers a complementary perspective for a more balanced debate in policymaking and industrial strategy. This article is part of the discussion meeting issue 'Sustainable metals: science and systems'.

Integrating hybrid deep learning and path allocation for real-time inbound passenger flow prediction and anomaly detection in urban rail transit

This paper study the problem of real-time prediction of inbound passenger flow and the detection and alerting of abnormal passenger flows in urban rail transit (URT) networks. We propose a fused framework that combines a hybrid deep learning model and an evaluation strategy. Specifically, the learning model incorporates Graph Convolutional Networks (GCN), Gated Recurrent Units (GRU), and attention mechanisms to effectively capture spatial and temporal correlations in passenger flow data. The evaluation strategy utilizes a depth-first search algorithm to determine the optimal travel paths for each individual passenger. And based on the paths, we develop a real-time method for estimating the origin–destination (OD) matrix that utilizes both long-term and short-term historical destination trend vectors to reduce dimensions while improving predictive accuracy. Through extensive testing using data from the Shanghai rail transit system, we demonstrate that this fused framework achieves high prediction accuracy for inbound passenger flow at various stations while efficiently identifying and warning sudden large-scale events involving significant increases in passenger flow volume. This research contributes towards improving overall passenger experience as well as operational resilience within urban rail systems when dealing with large-scale influxes of passengers.

Long Short-Term Memory Algorithm for Personalized Tacrolimus Dosing: A Simple and Effective Time Series Forecasting Approach Post-Lung Transplantation.

Management of tacrolimus trough levels influences morbidity and mortality after lung transplantation. Several studies have explored pharmacokinetic and artificial intelligence models to monitor tacrolimus levels. However, many models depend on a wide range of variables, some of which, like genetic polymorphisms, are not commonly tested for in regular clinical practice. This study aimed to verify the efficacy of a novel approach simply utilizing time series data of tacrolimus dosing, with the objective of accurately predicting trough levels in the variety of clinical settings. Data encompassing 36 clinical variables for each patient were gathered, and a multivariate long short-term memory algorithm was applied to forecast subsequent tacrolimus trough levels based on the selected clinical variables. The tool was developed using a dataset of 87,112 data points from 117 patients and its efficacy was confirmed using six additional cases. Shapley Additive exPlanations revealed a significant correlation between trough levels and prior dose-concentration data. By using simple trend learning of dose, administration route, and previous trough levels of tacrolimus, we could predict values within 30% of the actual values for 88.5% of time points, which facilitated the creation of a tool for simulating tacrolimus trough levels in response to dosage adjustments. The tool exhibited the potential for rectifying clinical misjudgments in a simulation cohort. Utilizing our time series forecasting tool, precise prediction of trough levels is attainable independently of other clinical variables, through the analysis of historical tacrolimus dose-concentration trends alone.

Response of drought to climate extremes in a semi-arid inland river basin in China

AbstractAgainst the backdrop of global warming, climate extremes and drought events have become more severe, especially in arid and semi-arid areas. This study forecasted the characteristics of climate extremes in the Xilin River Basin (a semi-arid inland river basin) of China for the period of 2021–2100 by employing a multi-model ensemble approach based on three climate Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6). Furthermore, a linear regression, a wavelet analysis, and the correlation analysis were conducted to explore the response of climate extremes to the Standardized Precipitation Evapotranspiration Index (SPEI) and Streamflow Drought Index (SDI), as well as their respective trends during the historical period from 1970 to 2020 and during the future period from 2021 to 2070. The results indicated that extreme high temperatures and extreme precipitation will further intensify under the higher forcing scenarios (SSP5-8.5>SSP2-4.5>SSP1-2.6) in the future. The SPEI trends under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios were estimated as −0.003/a, −0.004/a, and −0.008/a, respectively, indicating a drier future climate. During the historical period (1970–2020), the SPEI and SDI trends were −0.003/a and −0.016/a, respectively, with significant cycles of 15 and 22 a, and abrupt changes occurring in 1995 and 1996, respectively. The next abrupt change in the SPEI was projected to occur in the 2040s. The SPEI had a significant positive correlation with both summer days (SU) and heavy precipitation days (R10mm), while the SDI was only significantly positively correlated with R10mm. Additionally, the SPEI and SDI exhibited a strong and consistent positive correlation at a cycle of 4–6 a, indicating a robust interdependence between the two indices. These findings have important implications for policy makers, enabling them to improve water resource management of inland river basins in arid and semi-arid areas under future climate uncertainty.

Assessment of heating degree-days factor (HDDF) for building-oriented climate zoning in Türkiye

ABSTRACT In this study, a novel heating degree-days factor (HDDF) is proposed to determine the optimum facade design for buildings in Türkiye as a function of both cooling degree-days (CDD) and heating degree-days (HDD) data. For this purpose, the HDDF (HDD/(HDD+CDD)) is defined as the ratio of the heating degree-days of a region to the total heating and cooling degree-days. Firstly, the HDDF values of all provinces in Türkiye are calculated by using meteorological data from 81 meteorological stations covering the period 1991–2022, and HDDF performance is evaluated. Then, the building-oriented climate zoning is realized based on the HDDF values. Moreover, historical trends of monthly and yearly average HDD, CDD, and HDDF values are revealed and yearly HDD, CDD, and HDDF maps were created using the ArcGIS program. According to the results, HDDF values in Türkiye varied between 0.4519 and 0.9998, and HDDF values between 0.9 ≤ HDDF ≤ 1 cover 46 out of 81 cities in the country and span a broad area across the country. The findings suggest that if global warming continues at the current rate, cooling energy demand in the residential sector will dramatically increase in the future, while heating energy demand will fall in Türkiye. This indicates that domestic energy consumption regulations would require modification. The building-oriented climate zoning resulting from this study is unique to Türkiye and can aid engineers, architects, and policymakers in building energy efficiency programs, studies, regulations, and standards.

Artificial Intelligence in Life Expectancy Prediction: A Paradigm Shift for Annuity Pricing and Risk Management

This article explores the transformative potential of artificial intelligence (AI) in predicting life expectancy and its far-reaching implications for the annuities market. Traditional actuarial models, often reliant on demographic data and historical trends, face limitations in accuracy and personalization. We present a novel approach leveraging machine learning algorithms, including neural networks, decision trees, and ensemble methods, alongside natural language processing and deep learning techniques. Our AI-driven models integrate diverse data sources, including medical histories, genetic information, lifestyle factors, and socio-economic indicators, to provide more accurate and individualized life expectancy predictions. Using a dataset of anonymized health records and historical mortality data, we demonstrate that our AI models significantly outperform traditional actuarial tables in predicting individual mortality risks. The enhanced predictive power of these models has substantial implications for the annuities market, enabling insurers to price products more precisely, manage longevity risk more effectively, and optimize reserve capital. Moreover, consumers benefit from fairer pricing and personalized product offerings. This article underscores the need for regulatory framework revisions to accommodate AI-driven methodologies in actuarial practices. We also discuss ethical considerations, data privacy concerns, and the challenge of model interpretability, highlighting areas for future research to ensure responsible deployment of AI in life expectancy predictions and annuity pricing