Next Article in Journal
Impacts of REDD+ on Forest Conservation in a Protected Area of the Amazon
Previous Article in Journal
Maximizing Value in Constructed Wetlands: A Review of Ornamental Plants for Wastewater Treatment and Artisanal Applications
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Earth
Volume 6
Issue 4
10.3390/earth6040127
earth-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Shifting Electricity Demand Under Temperature Extremes in Bangladesh

by
Md. Mahbub Alam
1,
Sharad Aryal
2 and
Quazi K. Hassan
2,*
1
Department of Physics, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh
2
Department of Geomatics Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada
*
Author to whom correspondence should be addressed.
Earth 2025, 6(4), 127; https://doi.org/10.3390/earth6040127
Submission received: 12 September 2025 / Revised: 6 October 2025 / Accepted: 14 October 2025 / Published: 15 October 2025
Browse Figures
Versions Notes

Abstract

Bangladesh is among the world’s most climate-vulnerable countries, facing recurrent hazards that disrupt lives and livelihoods. Among these, heatwaves and cold snaps strongly affect electricity consumption, representing a key socio-economic impact of climate extremes. In this study, we used meteorological and electricity data from six sub-regions of Bangladesh to examine long-term changes in extreme temperature days and their effects on electricity usage. Results showed that western inland stations (Chuadanga, Jashore) experienced hotter summers and colder winters, whereas coastal sites (Barishal, Patuakhali) were moderated by maritime influences. Trend analysis revealed significant increases in hot-day frequency since 1961 (up to 1.8 days yr−1 at coastal areas, while cold-day frequencies generally declined but with regional variability. Electricity demand followed a clear pattern, being highest on hot days, lowest on cold days, and intermediate on normal days. Among the regions, Khulna consistently recorded the greatest demand (up to 161 MWh), while Patuakhali remained the lowest (~19–32 MWh). Regression analysis further showed that demand rises with maximum temperature, with slopes up to 5.7 MWh °C−1 and moderate correlations (r = 0.27–0.47). Importantly, the temperature–demand relationship has strengthened in recent years, as similar climatic conditions now correspond to higher electricity use, reflecting both climatic pressures and socio-economic growth. These findings highlight the challenge of temperature extremes for electricity demand and the need to integrate climate–energy linkages into adaptation planning.

1. Introduction

Bangladesh is widely recognized as one of the most climate-vulnerable countries in the world due to its geographic position, monsoon-dominated tropical climate, high population density, dependence on agriculture, rapid urban growth, and recurrent climate-induced disasters [1,2,3]. The country is frequently exposed to climate change–induced hazards such as heatwaves, cyclones, floods, droughts, tidal surges, waterlogging, salinity intrusion, riverbank erosion, and coastal erosion [4,5,6,7,8]. These hazards not only threaten lives and public health but also drive substantial social and economic disruptions, undermining national resilience [5,9,10,11]. Among the most affected sectors is energy production and consumption, which is highly sensitive to climate variability. The energy sector, already under pressure from rapidly increasing demand, is particularly vulnerable to extreme temperatures that influence consumption behavior while placing additional stress on generation and distribution systems.
Global climate change has accelerated the frequency and severity of extreme events [12,13]. Over the past decade, the Earth has experienced record-breaking warmth, with each year from 2015 to 2024 listed among the hottest in history [14,15]. A clear outcome of this warming is the shift in temperature extremes, with heatwaves increasing and cold extremes declining worldwide [12,16]. Events that were once rare are now occurring with increasing regularity [12,17]. These global patterns are mirrored in Bangladesh and surrounding South Asian regions, where a growing body of evidence points to rising heat extremes and a decline in cold events [1,8,18,19,20,21,22,23]. For instance, Islam et al. [1] reported a rise in heat extremes and a decline in cold extremes at 20 meteorological stations across Bangladesh between 1980 and 2017. Similarly, Abdullah et al. [4] documented significant increases in warm spells and decreases in cold spells at 13 meteorological stations during 1968–2018 in Bangladesh. Their analysis also highlighted spatial variability, with coastal areas experiencing more pronounced changes compared to inland regions.
Extreme temperature events exert far-reaching impacts on human well-being, ecosystems, and socioeconomic systems across the globe [10,12,24,25]. Heatwaves and cold snaps are particularly damaging, often leading to sharp rises in illness and mortality among vulnerable populations [26,27,28,29]. For example, the summer of 2022 saw Europe experience over 60,000 heat-related deaths [30], while the unprecedented 2021 heatwave in British Columbia, Canada, was associated with more than 600 fatalities and a marked rise in hospitalizations [26,27]. Cold extremes also remain a persistent concern, with an estimated 230 deaths per year in India between 1978 and 2014 attributable to cold waves [31]. In Bangladesh, cold-related mortality has likewise been significant, averaging about 104 deaths annually from 2009 to 2021 [10]. Beyond this, environmental and agricultural systems are equally vulnerable. Extreme temperatures have been associated with an increased incidence of forest fires [24], declines in river water quality [32], disruptions in hydrological processes [33], biodiversity loss [34], reductions in gross primary productivity [35], and decreased crop yields [36].
The energy sector represents a critical interface where climate extremes directly translate into economic and social impacts. Electricity demand is highly sensitive to both hot and cold extremes [37,38,39,40]. On such extreme days, the demand for heating and cooling rises sharply, driving greater reliance on temperature-regulating appliances such as air conditioners and heaters. This, in turn, leads to substantially higher electricity consumption compared to normal conditions [41]. Recent studies have reported that extreme heat can increase cooling degree days by more than 100% in densely populated regions [42], while cold snaps have been associated with sharp demand surges across North America [38]. Furthermore, a U-shaped, non-linear relationship between temperature and electricity demand has been observed in various regions, highlighting the dual influence of both hot and cold extremes on energy use [37,41,43,44,45]. Beyond demand, climate extremes also compromise electricity supply by lowering the efficiency of fossil fuel power plants, constraining hydropower generation, and reducing the reliability of renewable energy performance [46,47,48,49,50]. They can further damage transmission and distribution infrastructure [46,47], amplifying the overall vulnerability of energy systems.
A recent study in Tennessee, USA, identified a nonlinear U-shaped relationship between temperature and electricity demand, with higher consumption driven by space heating in winter and space cooling in summer [43]. Using a linear regression model, the study projected that summer electricity demand could increase by up to 20% by the end of the century, while winter demand is generally expected to decline. Romitti and Sue Wing [45] analyzed the temperature–demand relationship across 36 global cities and found considerable heterogeneity among them. They identified three general patterns, V-shaped, unresponsive, and increasing relationships, with the specific form influenced by climatic conditions, level of development, and socio-economic factors. Meanwhile, in the Southern USA, seasonal variability in electricity demand was found to be strongly influenced by air temperature, with temperature changes explaining 44–67% of the variation in demand [51].
Beyond climatic influences, changing socio-economic conditions and population growth can also shape the complex relationship between temperature and energy consumption [37,39]. Gupta [37] highlighted that the threshold temperature at which electricity demand begins to rise can shift with socio-economic development. As household income and living standards improve, people are more likely to afford and use appliances such as air conditioners and heaters, thereby intensifying electricity demand and altering the temperature–demand relationship. Moreover, population growth and urbanization can further modify both the magnitude and sensitivity of electricity demand to temperature variability, as expanding urban populations place greater pressure on cooling and heating needs [52]. In the context of Bangladesh, these dynamics are particularly relevant. The country is one of the fastest-growing economies, transitioning from a primarily agriculture-based system to one driven by industry and services [3]. Rapid urbanization has significantly reshaped settlement patterns, with the share of the population living in cities increasing from 20% in 1990 to 38% in 2020, and projections indicating that nearly 60% will reside in urban areas by 2050 [3,53]. These socio-economic and demographic transformations are likely to further intensify electricity demand during periods of temperature extremes.
The climate projections for Bangladesh and surrounding regions indicate that climate extremes are likely to intensify in the coming decades [54,55,56]. Choi et al. [54] projected an increase in the frequency and severity of heatwaves between 2021 and 2050, while Jihan et al. (2025) [55] estimated a rise in maximum temperature of up to 4.1 °C by 2100. Moreover, population growth and rising economic status are projected to continue in Bangladesh in the near future [57]. These developments are expected not only to escalate overall energy demand but also to reshape the temperature–demand relationship. Understanding the interplay between temperature, electricity demand, and evolving socio-economic conditions is therefore critical. Such insights can support the energy sector in adapting to a changing climate and in preparing more effectively to meet future demand.
Despite growing evidence of the strong links between extreme temperature and electricity demand, limited studies have systematically examined these relationships in Bangladesh, where climatic vulnerability and rapid socio-economic change converge. This study aims to fill this gap by identifying extreme hot and cold days using percentile-based thresholds, and by examining how electricity demand responds to these extremes. We further analyze temporal shifts in demand sensitivity between an early period (2020–2021) and a more recent one (2023–2024), providing insights into how socio-economic and climatic changes are reshaping the temperature–demand relationship. The rest of the manuscript first outlines the data and methods used in the analysis, then presents the main results, followed by a discussion of their implications. The paper concludes with key findings and reflections for future energy planning under climate change in Bangladesh.

2. Materials and Methods

2.1. Study Area

Bangladesh is a deltaic country shaped by the confluence of the Ganges, Brahmaputra, and Meghna rivers, situated between the Himalayas to the north and the Bay of Bengal to the south [58]. It covers an area of 147,570 km2 and is administratively divided into 8 divisions and 64 districts. This study focuses on the southwestern part of the country, where changes in climatic extremes and their implications for electricity usage were analyzed across six subregions (Figure 1 and Table 1). These subregions collectively represent 21 districts, covering nearly 30% of Bangladesh and lie predominantly south of the Ganges/Padma riverbank. The area is home to more than 33 million people, with a population density of approximately 800 people per square kilometer. The topography is characteristically flat, with most areas located below 20 m above mean sea level.
The climate of Bangladesh is tropical monsoon and can be divided into four distinct seasons: winter (December–February), summer (March–May), monsoon (June–September), and autumn (October–November) [4]. Winters are generally dry, summers hot and humid, and the majority of annual precipitation occurs during the monsoon season. Within the seven sub-climatic zones of Bangladesh, the study area spans portions of the southwestern, south-central, and southeastern regions [59] which are characterized by mild to hot summers and moderate to heavy rainfall [60]. The average air temperatures range from about 17 °C in January to nearly 30 °C in May [61], while annual rainfall varies between 1700 mm and 2600 mm [62]. The region’s land use is primarily agricultural, supplemented by substantial vegetation cover, while water bodies, wetlands, bare soils, and urban settlements make up the remaining landscape [63]. Economically, agriculture and manufacturing industries contribute substantially to the national economy. In terms of energy, Bangladesh had an installed grid-based capacity of 27,740 MW as of October 2024, achieving 100% electrification in 2022 [64]. The power sector is heavily reliant on fossil fuels, with natural gas accounting for nearly 68% of electricity generation and furnace oil for 21%, while renewable energy sources provide only a limited contribution [65].

2.2. Data and Methods

The methodological framework employed in this study is outlined in Figure 2. Two key datasets were utilized: meteorological observations (daily maximum, minimum, and mean air temperature) and daily electricity load for six planning regions of Bangladesh. Meteorological records were obtained from the Bangladesh Meteorological Department (BMD) for the period 1961–2024. Among the selected stations, Khulna, Jashore, Faridpur, and Barishal provided continuous records from 1961 onward, whereas Chuadanga (available from 1991) and Patuakhali (available from 1981) offered shorter observation periods. Quality control of the meteorological data followed the procedures described by Alam et al. [66]. Electricity load data, spanning 2020–2024, were obtained from West Zone Power Distribution Company Ltd. (WZPDCL), Khulna, Bangladesh. Together, these datasets enabled the identification of extreme temperature days, assessment of their long-term frequency, and analysis of the temperature–demand relationship.

2.2.1. Identification of Extreme Temperature Days

To characterize temperature extremes, a percentile-based approach was applied using the baseline period 1961–1990 (or the earliest 30 years of record). Percentile-based methods have been known to represent local conditions better than fixed thresholds [67]. Hot days were defined as days when the daily maximum temperature exceeded the 90th, 95th, or 99th percentiles of the baseline distribution (TX90, TX95, TX99). Conversely, cold days were identified when the daily minimum temperature fell below the 10th, 5th, or 1st percentiles (TN10, TN5, TN1). Days that did not meet either condition were classified as normal days. This classification was applied separately for each percentile pair (e.g., TX90–TN10, TX95–TN5, TX99–TN1).

2.2.2. Trend Analysis of Extreme Events

The temporal trends in the annual frequency of hot and cold days were examined using the non-parametric Mann–Kendall (MK) test, which is widely used to detect monotonic trends in climate time series without assuming data normality [68]. To quantify the magnitude of change, Sen’s slope estimator was applied, providing an estimate of the rate of increase or decrease in the number of extreme events per year. Statistical significance was assessed at the 95% and 99% confidence levels.

2.2.3. Temperature–Demand Relationship

The influence of temperature on electricity consumption was evaluated through linear regression analysis. For hot days, the daily maximum temperature was compared against daily electricity load, while for cold days, daily minimum temperature was used. The slope of the regression line reflected the rate of change in demand per degree Celsius, and the correlation coefficient (r) was calculated to assess the strength of the association.

2.2.4. Temporal Shifts in Demand Sensitivity

To examine whether the temperature–demand relationship has changed in recent years, regression slopes and correlation coefficients were compared between two sub-periods: 2020–2021 and 2023–2024. This comparison allowed the detection of shifts in baseline consumption and sensitivity to climatic extremes.

3. Results

3.1. Climate Extreme and Their Trends

Table 2 presents the temperature thresholds that define hot and cold days for six meteorological stations. Clear spatial variations can be observed across the region. The western stations, particularly Chuadanga and Jashore, recorded the highest hot-day thresholds, with TX99 values of 40.0 °C and 39.6 °C, highlighting their exposure to more intense summer heat. By contrast, the eastern coastal stations of Barishal and Patuakhali showed noticeably lower hot-day thresholds; for example, Barishal had a TX90 of 33.9 °C, while Patuakhali recorded 34.3 °C. Cold extremes followed a similar pattern of variation. Chuadanga showed the lowest winter thresholds, with TN1 dropping to 7.2 °C, pointing to harsher winter conditions. In Patuakhali, however, the cold-day thresholds were much milder, with TN10 at 14.3 °C and TN1 at 10.7 °C, the warmest among the stations. Faridpur and Khulna fell between these two groups, showing relatively high hot-day thresholds but less severe cold extremes compared to Chuadanga and Jashore. The results indicated that the western inland stations were more prone to extreme summer heat and colder winters, whereas the coastal stations on the eastern side experienced a narrower range between hot and cold thresholds, reflecting their more moderated climate.
The annual frequency of hot days based on maximum temperature (Table 3a) exhibited significant upward trends at all stations during both 1961–2024 and 1991–2024, with the increases being stronger in the more recent period. The sharpest rises were recorded at the eastern coastal stations, particularly Patuakhali (1.78 days/yr for TX90 during 1981–2024; 2.00 days/yr during 1991–2024) and Barishal (1.13 and 1.62 days/yr, respectively), both significant at the 99% confidence level. Faridpur also showed a marked acceleration in recent decades, with rates nearly double those observed over the longer baseline. Khulna and Jashore experienced moderate but statistically significant upward trends, whereas Chuadanga showed little to no change, including a few statistically non-significant declines. At the same time, the frequency of cold days (Table 3b) generally declined, though the trends were weaker and less uniform across stations. Notably, Khulna, Barishal, and Patuakhali displayed contrasting patterns between the two periods. While long-term trends at these locations indicated slight increases in cold-day frequency (up to 0.21 days/yr), the recent period showed clear declines (−0.16 to −0.72 days/yr), with the decreases at Khulna being statistically significant. Cold days also declined at Jashore and Faridpur, with Faridpur recording a significant long-term decrease in TN10-based cold days (−0.31 days/yr, significant at the 99% level). In contrast, Chuadanga stood out for its notably different behavior, showing weak but consistently positive, though statistically insignificant, trends in cold-day frequency across all thresholds during 1991 to 2024.

3.2. Electricity Usage and Its Relationship with Temperature

The average electricity consumption on hot days exceeded that of both normal and cold days across all six sub-regions (Table 4). A consistent pattern emerged in which normal days also showed higher demand than cold days, indicating the influence of warmer temperatures on energy use. Among the stations, Khulna recorded the highest electricity usage under all conditions, with average hot-day demand ranging from 149.1 MWh to 161.1 MWh depending on the threshold applied. Even on normal and cold days, Khulna’s consumption remained the highest, with values of 123.6–123.7 MWh and 99.5–101.3 MWh, respectively. Meanwhile, Patuakhali exhibited the lowest demand, with peak usage of only 32.2 MWh on TX99-based hot days and about 19 MWh on cold days.
For the remaining four locations, hot-day demand generally ranged between 73.8 MWh and 114.8 MWh, while cold-day demand was substantially lower, between 42.7 MWh and 67.7 MWh. Normal-day consumption fell between these two extremes (58.0–85.3 MWh). Importantly, demand rose consistently with higher threshold definitions, highlighting the strong link between extreme temperatures and electricity usage. For instance, in Kushtia, average hot-day demand increased from 104.0 MWh under TX90 to 114.8 MWh under TX99. Similar upward shifts were also observed for cold-day consumption, though changes were relatively smaller in magnitude.
The electricity demand shows a clear sensitivity to temperature, as depicted by the fitted demand–temperature curves (Figure 3). Across all six sub-regions, demand increases nonlinearly with rising temperatures, with the steepest growth usually occurring once mean temperature exceeds 25 °C. During the early period (2020–2021), consumption levels remained consistently lower. In contrast, both the extended record (2020–2024) and the most recent year (2023–2024) displayed higher electricity use at comparable temperatures. The non-linear rise is particularly evident in hotter conditions, reflecting stronger demand peaks. The upward shift indicates an elevated baseline of electricity consumption at all subregions.
The electricity load increased linearly with maximum temperature during hot days across all six sub-regions, as shown by the positive regression slopes and correlation coefficients (Figure 4). The strength of this relationship varied depending on the temperature threshold used to define hot days. For TX90-based events, correlation coefficients generally ranged from 0.27 to 0.39, while TX95-based days showed values between 0.33 and 0.37. The association strengthened further under TX99, where correlations spanned from 0.27 to 0.54, highlighting that the link between temperature and electricity demand becomes more pronounced during more extreme heat conditions. Khulna consistently displayed higher correlations for both TX90- and TX95-based days, whereas the strongest overall relationship was detected in Kushtia under TX99 (r = 0.54). The regression slopes, representing the rate of increase in electricity demand per degree rise in maximum temperature, ranged from 1.31 to 6.28 MWh/°C. Larger slopes were generally observed in higher-demand areas such as Khulna, while smaller slopes were found in lower-demand regions like Patuakhali. The steepest response occurred in Faridpur, where each 1 °C increase on TX99-based hot days corresponded to an additional 6.28 MWh of electricity use, underscoring the impact of extreme heat on regional energy demand.
The relationship between daily minimum temperature and electricity demand during cold days was weak and spatially inconsistent across the six sub-regions (Figure 5). The direction of response also varied with threshold. During TN10-based cold days, Faridpur and Khulna showed increased demand as temperatures decreased, though the correlations were small (r = −0.04 to −0.09) with slopes of 0.14 MWh °C−1 at Faridpur and 0.70 MWh °C−1 at Khulna. In contrast, Barishal, Jashore, Kushtia, and Patuakhali showed declining demand at lower temperatures, with positive correlations (r = 0.04 to 0.16) and a maximum slope of 0.73 MWh °C−1 in Kushtia. Faridpur exhibited a consistent positive association across all thresholds (r = −0.04 to −0.52), whereas Patuakhali showed decreasing demand at all thresholds (r = 0.05 to 0.44). For TN5-based cold days, the response remained mixed Barishal and Faridpur indicated higher electricity demand with falling temperatures, while Jashore, Khulna, Kushtia, and Patuakhali exhibited the opposite direction. For TN1-based cold days, the limited number of extreme cold days produced very high correlation coefficients, particularly in Jashore and Khulna, where r values approached 1. Regression slopes at TN1 were also larger, such as 8.47 MWh °C−1 at Khulna and −4.70 MWh °C−1 at Faridpur, reflecting stronger relationships under rare extreme events. Overall, both correlation and slope values tended to increase at more extreme thresholds, but the sign and magnitude of the relationship differed substantially by location.
The relationship between daily maximum temperature and electricity demand has changed over time, as demonstrated in Figure 6. In all subregions, the regression lines for 2023–2024 shifted upward relative to 2020–2021, indicating higher baseline demand for comparable hot-day conditions. Although baseline demand increased across all six sub-regions, the strength and magnitude of the relationship with temperature varied spatially. At Barishal, Faridpur, and Patuakhali, both slope and correlation coefficient increased in recent years. Correlation values that ranged between −0.01 and 0.49 during 2020–2021 rose to 0.54–0.61 in 2023–2024. The slopes showed a similar strengthening, with the largest change observed in Faridpur where the slope increased from –0.04 MWh °C−1 in 2020–2021 to 2.50 MWh °C−1 in 2023–2024. By contrast, Jashore, Khulna, and Kushtia exhibited reduced sensitivity in 2023–2024 compared to 2020–2021. In Khulna, the slope decreased markedly from 6.58 MWh °C−1 to 2.09 MWh °C−1, while the correlation coefficient declined from 0.74 to 0.34. A similar weakening was observed in Jashore and Kushtia, though less pronounced. In Khulna, the two regression lines converge at higher temperature extremes, suggesting that electricity demand may have been constrained by supply limitations rather than reflecting reduced sensitivity, consistent with Khulna’s position as the highest-consuming sub-region.
Figure 7 presents the linear regression relationships between daily electricity demand and daily minimum temperature during TN10-defined cold days for the periods 2020–2021 and 2023–2024. Overall, cold-day demand responses remained weak and spatially inconsistent, with only modest variations over time. Similar to the hot-day case, baseline demand increased across all sub-regions, as indicated by upward shifts in the regression lines between the two periods. However, the slopes and correlation coefficients remained generally weak and comparable across both periods, indicating limited changes in the sensitivity of electricity usage to cold temperatures. At Faridpur, Jashore, Khulna, and Patuakhali, slopes and correlation coefficients were nearly identical between the two periods, with the primary difference being an overall increase in baseline demand. The strongest relationship was observed at Barishal in 2020–2021, where the correlation reached 0.41 with a slope of 0.78 MWh °C−1. By 2023–2024, both values declined, with the correlation dropping to 0.17 and the slope reducing to 0.30 MWh °C−1.

4. Discussion

The findings indicated that western inland areas such as Chuadanga experienced both hotter summers and colder winters, reflecting stronger climatic extremes, whereas coastal areas show more moderate thresholds. Coastal Bangladesh’s climate is shaped by the Indian Ocean and Bay of Bengal, while inland patterns are dictated by rainfall seasonality and the Himalayan Highlands–Tibetan Plateau [4]. The Bay of Bengal, with its large heat capacity, helps buffer coastal temperatures and reduce extremes [69]. This moderating effect is reinforced by land–sea breeze circulations that balance daytime heating and nighttime cooling [70], which explains the narrower temperature range for coastal areas. Our findings of higher hot-day thresholds in the western region align with Dastour et al. [19], who reported a longitudinal gradient in maximum air temperature, with values decreasing eastward, leaving the western areas relatively hotter. Beyond regional climatic controls, local factors such as land use, surface water bodies, vegetation cover, elevation, and urbanization also influence temperature variability [8,19,71,72,73]. In particular, the Urban Heat Island effect likely contributes to elevated temperatures in densely populated areas such as Faridpur and Kushtia.
The results showed significant increases in hot-day frequency across all stations, with sharper rises in the recent decades and particularly strong trends at the eastern coastal sites of Patuakhali and Barishal. In contrast, cold-day frequency generally declined, though trends were weaker and more variable, with Chuadanga exhibiting a distinct pattern of little change in hot days and weak positive cold-day trends. The findings are consistent with other similar studies in Bangladesh [4,8,19,73]. Abdullah et al. [4] documented an average increase of 0.39 days/year in warm days in coastal areas compared to 0.15 days/year in inland regions during 1968–2018, while Islam et al. [1] also found increases in hot-day indices alongside declining cold indices. They further highlighted the role of large-scale atmospheric oscillations such as the Atlantic Multidecadal Oscillation (AMO) in modulating temperature extremes over Bangladesh. More recently, Tabassum et al. [73] detected a significant rise in heatwaves in Dhaka, increasing by up to 5.8 days per decade during 1995–2019, and intensifying to 12.2 days per decade during 2008–2019 in the monsoon season. These heatwaves have been linked to high-pressure anomalies and tropospheric anticyclonic circulation, while other contributing factors include changes in geopotential height, declining wind speed, shifts in cloud cover, land-use changes, and urbanization, all of which influence the occurrence and severity of hot extremes across the country [1,8,73].
Higher electricity consumption during hot days across all six sub-regions underscores the strong influence of heat on energy demand. As shown in Table 2, hot-day temperatures often rise well above the mid-thirties, driving increased use of cooling appliances such as refrigerators, fans, and air conditioners. In addition, irrigation needs are higher during extremely hot days, leading to greater use of groundwater pumps. Since some of these pumps run on electricity [74], they contribute to higher electricity demand. Consequently, a clear and significant relationship emerged between maximum temperature and electricity usage during hot days. This association was consistently positive, with moderate correlations across the regions, and became more pronounced when more extreme heat thresholds were considered. These findings align with Arshad and Beyer [75], who reported that once temperatures exceed 20 °C, electricity demand rises by about 2.8% for each additional degree in Bangladesh. Similarly, a study in Dhaka found that a 1 °C increase in air temperature corresponded to an additional 81 MW of electricity demand, highlighting temperature as the single most influential factor shaping electricity consumption [76].
On the other hand, normal days also showed higher consumption than cold days, indicating that even moderate warmth elevates electricity use. Spatial contrasts were evident, as Khulna consistently recorded the highest demand under all thresholds, reflecting its high industrial concentration [77], whereas coastal Patuakhali showed the lowest demand, likely due to its smaller population and limited infrastructure. By comparison, electricity use during cold days was generally lower, and the relationship between minimum temperatures and demand appeared weaker and spatially inconsistent. Similar patterns have been reported elsewhere; for instance, Yang et al. [41] found that rising summer temperatures exerted a stronger influence on urban electricity consumption in China than declining winter temperatures. Higher electricity use in summer is typical in tropical and subtropical regions, where extreme heat leads to greater energy consumption. For instance, in Sydney, Australia, electricity demand during summer is already higher [78] and is projected to increase by up to 11.3% by the end of the century, while similar projections of rising summer demand are also reported in Tennessee, USA [43]. Our findings align with those of Romitti and Sue Wing [45] who reported that populous tropical cities such as Manila and Delhi exhibit a similar temperature–demand curve, with electricity demand primarily driven by hot temperatures. In contrast, mid- to high-latitude developed cities such as Tokyo and Colorado demonstrated sensitivity to both cold and hot conditions.
In Bangladesh, the tropical climate and low elevations mean that winters are relatively mild, with minimum temperatures rarely falling below 10 °C. The weak and mixed correlations therefore suggest that cold extremes are not a major driver of electricity demand, unlike in higher-latitude regions [38,45], and that consumption is more strongly tied to cooling needs than to heating or lighting. Residential and commercial infrastructure is primarily designed to withstand extreme heat, while heating appliances are uncommon, resulting in relatively low winter electricity use. Moreover, many households rely on firewood and other non-electric energy sources for heating, and even in buildings equipped with electric appliances, usage is far less frequent than that of cooling devices in summer. Consequently, winter overcapacity has become a recurring challenge for the Bangladesh power sector [64].
Importantly, temporal shifts in the temperature demand relationship indicate that baseline electricity consumption has risen in recent years. This observation is consistent with Arshad and Beyer [75], who reported substantial growth in Bangladesh’s electricity use between 1993 and 2021, accompanied by marked seasonal and diurnal variation. Beyond meteorological influences, socio-economic factors such as population growth, urbanization, increase in GDP, and wider usage of electrical appliances likely explain the elevated baseline demand observed during 2023–2024, suggesting that both climatic and socio-economic changes are intensifying electricity needs. Residential consumption represents the largest share of gridded electricity use, with households accounting for more than half of total demand in fiscal year 2022–2023, compared to less than 28% for industry [64]. This pattern underscores the dominant role of population-driven residential demand in shaping national consumption trends. Access to electricity and improved economic conditions also play a significant role in determining per capita usage [75]. Bangladesh’s GDP increased steadily from 2020 to 2024 and is projected to continue growing [79], reinforcing this rising trend. In addition, the COVID-19 pandemic likely influenced recent dynamics, as the 2020/2021 period coincided with widespread lockdowns that reduced overall electricity consumption [75], thereby widening the gap in the temperature–demand relationship between the two periods.
Although rising slopes and correlation coefficients in some areas indicate greater sensitivity of electricity demand to maximum temperatures, the recent flattening of slopes in Khulna and Kushtia likely reflects supply-side constraints rather than reduced cooling needs, given their already high baseline consumption. Persistent supply deficits in the national grid have resulted in frequent load-shedding. A recent report by the Institute for Energy Economics and Financial Analysis (IEEFA) noted that the supply shortfall peaked at 12.6% in April 2024, with households and industries experiencing at least 23 days of load-shedding per month during the non-winter period of fiscal year 2023/2024 [64]. Limited infrastructure capable of supporting high electricity use may also have slowed the growth in demand, contributing to the observed flattening of slopes and weaker correlations. In addition, significant energy losses during transmission and distribution further strain the already imbalanced demand–supply system, highlighting structural inefficiencies in Bangladesh’s power sector.
Despite the valuable insights gained, this study is not without limitations. First, the electricity load data cover only a relatively short period compared to the multi-decadal meteorological record, which limits the ability to fully assess long-term climate–demand interactions. Second, each electricity planning area was represented by a single nearby meteorological station, an approach that may not adequately capture the spatial variability of temperature across such large and diverse regions. This simplification could overlook important local differences in climate exposure, particularly in areas with contrasting land cover, elevation, or coastal–inland characteristics. Third, the analysis was based on aggregated electricity consumption, whereas sector-specific data (residential, commercial, industrial, agricultural) would provide a clearer picture of the drivers of temperature-sensitive demand. Fourth, using daily averages may conceal important intra-day fluctuations, as hourly data are often critical for understanding peak demand and grid stress. Moreover, other meteorological variables such as relative humidity, wind speed, and solar radiation, which can strongly influence cooling and heating needs, were not explicitly considered. Finally, the study does not account for potential effects of evolving technology, such as the increasing usage of air conditioners, efficiency improvements, or policy interventions, all of which could reshape future demand patterns. Addressing these limitations through longer datasets, multi-station climate representation, higher-resolution (hourly) analysis, and sectoral breakdowns would provide a more complete understanding of climate–energy nexus in Bangladesh.
Future climate projections indicate extremes, particularly heatwaves, will continue to intensify in Bangladesh [54,55], placing additional stress on the electricity system. At the same time, rapid population growth, urbanization, and economic expansion are expected to further increase electricity demand, with the sharpest rises likely to occur during the summer peak periods. National projections already indicate a sharp rise in electricity requirements by 2030, even under scenarios that incorporate efficiency measures and conservation policies [64]. The added stress of climatic extremes may amplify these estimates and increase uncertainty. Our results highlight that temperature-sensitive demand, especially for cooling, will be a critical driver of this growth, while the influence of cold extremes may remain relatively minor. Preparing the energy sector to meet this challenge will require a dual focus on expanding supply capacity and managing demand. Investments in grid reliability, renewable integration, and transmission efficiency, coupled with demand-side measures such as promoting energy-efficient cooling technologies, will be critical. Equally important is the development of climate-informed energy planning frameworks that explicitly consider the influence of extreme weather on electricity use. Such strategies would not only enhance the resilience of Bangladesh’s power system but also ensure sustainable and reliable access to energy in the face of a warming climate.

5. Conclusions

This study examined extreme temperature events, their temporal trends, and their relationship with electricity demand across six sub-regions of Bangladesh. To the best of our knowledge, this is the first work to systematically characterize the temperature–demand relationship in this region using daily temperature and electricity load records. The key conclusions are as follows:
  • Distinct spatial contrasts were observed; inland areas such as Chuadanga experienced stronger climatic extremes, while coastal regions like Patuakhali were moderated by maritime influences.
  • Hot-day frequencies increased notably across most sub-regions (by up to 2 days per year), whereas cold-day frequencies declined, reflecting the broader warming signal.
  • Electricity demand on hot days consistently exceeded that of normal or cold days, highlighting Bangladesh’s tropical climate, the widespread use of cooling appliances, and irrigation requirements.
  • The relationship between temperature and demand was non-linear, with very high maximum temperatures driving the steepest increases in electricity use.
  • Maximum temperature showed a strong and consistent influence, with correlation coefficients between 0.27 and 0.54, and demand increments of 1.31–6.28 MWh for every 1 °C rise in maximum temperature.
  • In contrast, cold extremes had only weak and inconsistent effects on electricity use, underscoring the limited role of electric heating in this context.
  • Between 2020 and 2024, a marked upward shift in the temperature–demand relationship was detected, linked to growing baseline demand from socio-economic development, rising population, and greater appliance usage.
  • Sub-regions such as Khulna and Kushtia exhibited a flattening of demand sensitivity, likely showing how load-shedding and supply-side constraints can mask underlying climate-driven demand patterns, pointing to the importance of infrastructure capacity.
Overall, these findings emphasize the need for future energy planning in Bangladesh to account for summer peak loads and resilience to prolonged heat events. With intensifying heatwaves, rapid urbanization, and continued economic growth, electricity demand will likely accelerate further, putting greater strain on the grid. Addressing these challenges requires not only expanding generation and transmission but also embedding climate-informed strategies into national planning. Investments in efficiency, demand-side management, and renewable integration will be essential to ensure reliable, resilient, and sustainable electricity supply in a warming climate.

Author Contributions

Conceptualization, M.M.A., S.A. and Q.K.H.; methodology, S.A. and Q.K.H.; software, M.M.A.; validation, S.A. and Q.K.H.; formal analysis, M.M.A. and S.A.; investigation, M.M.A.; resources, M.M.A. and Q.K.H.; data curation, M.M.A.; writing—original draft preparation, S.A.; writing—review and editing, M.M.A. and Q.K.H.; visualization, M.M.A. and S.A.; supervision, Q.K.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data will be available on request.

Acknowledgments

The authors would like to acknowledge Bangladesh Meteorological Department (BMD) and West Zone Power Distribution Company Ltd. (WZPDCL) for providing the required datasets for this study.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Islam, H.M.T.; Islam, A.R.M.T.; Abdullah-Al-Mahbub, M.; Shahid, S.; Tasnuva, A.; Kamruzzaman, M.; Hu, Z.; Elbeltagi, A.; Kabir, M.M.; Salam, M.A.; et al. Spatiotemporal Changes and Modulations of Extreme Climatic Indices in Monsoon-Dominated Climate Region Linkage with Large-Scale Atmospheric Oscillation. Atmos. Res. 2021, 264, 105840. [Google Scholar] [CrossRef]
  2. Adil, L.; Eckstein, D.; Künzel, V.; Schäfer, L. Climate Risk Index 2025; Germanwatch e.V.: Bonn, Germany, 2025. [Google Scholar]
  3. World Bank Group. Bangladesh–Country Climate and Development Report (English). 2022. Available online: http://documents.worldbank.org/curated/en/099510110202254063 (accessed on 3 October 2025).
  4. Abdullah, A.Y.M.; Bhuian, M.H.; Kiselev, G.; Dewan, A.; Hassan, Q.K.; Rafiuddin, M. Extreme Temperature and Rainfall Events in Bangladesh: A Comparison between Coastal and Inland Areas. Int. J. Climatol. 2022, 42, 3253–3273. [Google Scholar] [CrossRef]
  5. Dastagir, M.R. Modeling Recent Climate Change Induced Extreme Events in Bangladesh: A Review. Weather Clim. Extrem. 2015, 7, 49–60. [Google Scholar] [CrossRef]
  6. Kaiser, Z.R.M.A. Analysis of the Livelihood and Health of Internally Displaced Persons Due to Riverbank Erosion in Bangladesh. J. Migr. Health 2023, 7, 100157. [Google Scholar] [CrossRef]
  7. Shehab, M.K.; Haque, C.E.; Faisal, I.M.; Walker, D. Experience and Perception of Climatic Hazards as Drivers for Adaptation Strategies in Coastal Communities of Bangladesh. Int. J. Disaster Risk Reduct. 2025, 120, 105364. [Google Scholar] [CrossRef]
  8. Suborna, N.J.; Evan, R.I.; Rabby, M.J.; Rahman, M.; Khan, A.; Rakib, M.M.H.; Khan, R.; Hasan, I.; Roy, D.K. Urbanization Induced Heat Wave Risk Assessment of Khulna City, Bangladesh: A 30-Years Analysis of Land Surface Temperature, Climate Trends and Forecasting. Geosyst. Geoenviron. 2026, 5, 100448. [Google Scholar] [CrossRef]
  9. Alam, G.M.M.; Alam, K.; Mushtaq, S.; Sarker, M.N.I.; Hossain, M. Hazards, Food Insecurity and Human Displacement in Rural Riverine Bangladesh: Implications for Policy. Int. J. Disaster Risk Reduct. 2020, 43, 101364. [Google Scholar] [CrossRef]
  10. Alam, M.M.; Mahtab, A.S.M.; Ahmed, M.R.; Hassan, Q.K. Developing a Cold-Related Mortality Database in Bangladesh. Int. J. Environ. Res. Public Health 2022, 19, 12175. [Google Scholar] [CrossRef] [PubMed]
  11. Leal Filho, W.; Wolf, F.; Abubakar, I.R.; Al-Amin, A.Q.; Roy, S.; Malakar, K.; Alam, G.M.M.; Sarker, M.N.I. Understanding the Socio-Economic Impacts of Climate Change on Riparian Communities in Bangladesh. River Res. Appl. 2022, 38, 1884–1892. [Google Scholar] [CrossRef]
  12. Clarke, B.; Otto, F.; Stuart-Smith, R.; Harrington, L. Extreme Weather Impacts of Climate Change: An Attribution Perspective. Environ. Res. Clim. 2022, 1, 012001. [Google Scholar] [CrossRef]
  13. Ripple, W.J.; Wolf, C.; Gregg, J.W.; Rockström, J.; Mann, M.E.; Oreskes, N.; Lenton, T.M.; Rahmstorf, S.; Newsome, T.M.; Xu, C.; et al. The 2024 State of the Climate Report: Perilous Times on Planet Earth. BioScience 2024, 74, 812–824. [Google Scholar] [CrossRef]
  14. Esper, J.; Torbenson, M.; Büntgen, U. 2023 Summer Warmth Unparalleled over the Past 2000 Years. Nature 2024, 631, 94–97. [Google Scholar] [CrossRef] [PubMed]
  15. World Meteorological Organization (WMO). State of the Global Climate 2024; WMO-No. 1368; World Meteorological Organization: Geneva, Switzerland, 2025; ISBN 978-92-63-11368-9. [Google Scholar]
  16. Rajulapati, C.R.; Gaddam, R.K.; Nerantzaki, S.D.; Papalexiou, S.M.; Cannon, A.J.; Clark, M.P. Exacerbated Heat in Large Canadian Cities. Urban Clim. 2022, 42, 101097. [Google Scholar] [CrossRef]
  17. Robinson, A.; Lehmann, J.; Barriopedro, D.; Rahmstorf, S.; Coumou, D. Increasing Heat and Rainfall Extremes Now Far Outside the Historical Climate. NPJ Clim. Atmos. Sci. 2021, 4, 45. [Google Scholar] [CrossRef]
  18. Aryal, A.; Pandey, V.P.; Talchabhadel, R.; Thapa, B.R. Hydro-Climatic Extremes in a Medium Range River Basin in Western Nepal: Learning from Analysis of Observed Data. Stoch. Environ. Res. Risk. Assess. 2024, 38, 85–105. [Google Scholar] [CrossRef]
  19. Dastour, H.; Alam, M.M.; Dewan, A.; Hassan, Q.K. Evaluating Climatic Warming and the Modulating Effects of Surface Water and Regional Variables in Western Bangladesh. Results Eng. 2025, 25, 103864. [Google Scholar] [CrossRef]
  20. Khan, N.; Shahid, S.; Chung, E.-S.; Kim, S.; Ali, R. Influence of Surface Water Bodies on the Land Surface Temperature of Bangladesh. Sustainability 2019, 11, 6754. [Google Scholar] [CrossRef]
  21. Rahman, M.B.; Salam, R.; Islam, A.R.M.T.; Tasnuva, A.; Haque, U.; Shahid, S.; Hu, Z.; Mallick, J. Appraising the Historical and Projected Spatiotemporal Changes in the Heat Index in Bangladesh. Theor. Appl. Climatol. 2021, 146, 125–138. [Google Scholar] [CrossRef]
  22. Shahid, S.; Wang, X.-J.; Harun, S.B.; Shamsudin, S.B.; Ismail, T.; Minhans, A. Climate Variability and Changes in the Major Cities of Bangladesh: Observations, Possible Impacts and Adaptation. Reg. Environ. Change 2016, 16, 459–471. [Google Scholar] [CrossRef]
  23. Sharma, D.; Babel, M.S. Trends in Extreme Rainfall and Temperature Indices in the Western Thailand. Int. J. Climatol. 2014, 34, 2393–2407. [Google Scholar] [CrossRef]
  24. Dastour, H.; Ahmed, M.R.; Hassan, Q.K. Analysis of Forest Fire Patterns and Their Relationship with Climate Variables in Alberta’s Natural Subregions. Ecol. Inform. 2024, 80, 102531. [Google Scholar] [CrossRef]
  25. Vicedo-Cabrera, A.M.; Scovronick, N.; Sera, F.; Royé, D.; Schneider, R.; Tobias, A.; Astrom, C.; Guo, Y.; Honda, Y.; Hondula, D.M.; et al. The Burden of Heat-Related Mortality Attributable to Recent Human-Induced Climate Change. Nat. Clim. Change 2021, 11, 492–500. [Google Scholar] [CrossRef] [PubMed]
  26. British Columbia Coroners Service. Extreme Heat and Human Mortality: A Review of Heat-Related Deaths in B.C. in Summer 2021. 2022. Available online: https://www2.gov.bc.ca/assets/gov/birth-adoption-death-marriage-and-divorce/deaths/coroners-service/death-review-panel/extreme_heat_death_review_panel_report.pdf (accessed on 12 September 2025).
  27. Clark, D.G.; Jackson, E.H.; Hohl, C.M.; Liang, K.E. Extreme Heat Impacts on Acute Care: Examining Emergency Department Visits and Hospital Admissions during the 2021 British Columbia Heatwave. J. Clim. Change Health 2024, 17, 100310. [Google Scholar] [CrossRef]
  28. Gronlund, C.J.; Sullivan, K.P.; Kefelegn, Y.; Cameron, L.; O’Neill, M.S. Climate Change and Temperature Extremes: A Review of Heat- and Cold-Related Morbidity and Mortality Concerns of Municipalities. Maturitas 2018, 114, 54–59. [Google Scholar] [CrossRef] [PubMed]
  29. Paschalidou, A.K.; Kassomenos, P.A.; McGregor, G.R. Analysis of the Synoptic Winter Mortality Climatology in Five Regions of England: Searching for Evidence of Weather Signals. Sci. Total Environ. 2017, 598, 432–444. [Google Scholar] [CrossRef]
  30. Ballester, J.; Quijal-Zamorano, M.; Méndez Turrubiates, R.F.; Pegenaute, F.; Herrmann, F.R.; Robine, J.M.; Basagaña, X.; Tonne, C.; Antó, J.M.; Achebak, H. Heat-Related Mortality in Europe during the Summer of 2022. Nat. Med. 2023, 29, 1857–1866. [Google Scholar] [CrossRef]
  31. Malik, P.; Bhardwaj, P.; Singh, O. Distribution of Cold Wave Mortalities over India: 1978–2014. Int. J. Disaster Risk Reduct. 2020, 51, 101841. [Google Scholar] [CrossRef]
  32. Graham, D.J.; Bierkens, M.F.P.; Van Vliet, M.T.H. Impacts of Droughts and Heatwaves on River Water Quality Worldwide. J. Hydrol. 2024, 629, 130590. [Google Scholar] [CrossRef]
  33. Aryal, S.; Babel, M.S.; Gupta, A.; Farjad, B.; Khadka, D.; Hassan, Q.K. Attribution of the Climate and Land Use Change Impact on the Hydrological Processes of Athabasca River Basin, Canada. Hydrology 2025, 12, 7. [Google Scholar] [CrossRef]
  34. Reichstein, M.; Bahn, M.; Ciais, P.; Frank, D.; Mahecha, M.D.; Seneviratne, S.I.; Zscheischler, J.; Beer, C.; Buchmann, N.; Frank, D.C.; et al. Climate Extremes and the Carbon Cycle. Nature 2013, 500, 287–295. [Google Scholar] [CrossRef]
  35. Ciais, P.; Reichstein, M.; Viovy, N.; Granier, A.; Ogée, J.; Allard, V.; Aubinet, M.; Buchmann, N.; Bernhofer, C.; Carrara, A.; et al. Europe-Wide Reduction in Primary Productivity Caused by the Heat and Drought in 2003. Nature 2005, 437, 529–533. [Google Scholar] [CrossRef]
  36. Becker, R.; Schüth, C.; Merz, R.; Khaliq, T.; Usman, M.; Beek, T.A.D.; Kumar, R.; Schulz, S. Increased Heat Stress Reduces Future Yields of Three Major Crops in Pakistan’s Punjab Region despite Intensification of Irrigation. Agric. Water Manag. 2023, 281, 108243. [Google Scholar] [CrossRef]
  37. Gupta, E. Global Warming and Electricity Demand in the Rapidly Growing City of Delhi: A Semi-Parametric Variable Coefficient Approach. Energy Econ. 2012, 34, 1407–1421. [Google Scholar] [CrossRef]
  38. Millin, O.T.; Furtado, J.C.; Malloy, C. The Impact of North American Winter Weather Regimes on Electricity Load in the Central United States. NPJ Clim. Atmos. Sci. 2024, 7, 254. [Google Scholar] [CrossRef]
  39. Wang, C.; Song, J.; Shi, D.; Reyna, J.L.; Horsey, H.; Feron, S.; Zhou, Y.; Ouyang, Z.; Li, Y.; Jackson, R.B. Impacts of Climate Change, Population Growth, and Power Sector Decarbonization on Urban Building Energy Use. Nat. Commun. 2023, 14, 6434. [Google Scholar] [CrossRef]
  40. Zhang, M.; Chen, Y.; Hu, W.; Deng, N.; He, W. Exploring the Impact of Temperature Change on Residential Electricity Consumption in China: The ‘Crowding-out’ Effect of Income Growth. Energy Build. 2021, 245, 111040. [Google Scholar] [CrossRef]
  41. Yang, S.; Jahanger, A.; Awan, A. Temperature Variation and Urban Electricity Consumption in China: Implications for Demand Management and Planning. Util. Policy 2024, 90, 101782. [Google Scholar] [CrossRef]
  42. Morakinyo, T.E.; Ren, C.; Shi, Y.; Lau, K.K.-L.; Tong, H.-W.; Choy, C.-W.; Ng, E. Estimates of the Impact of Extreme Heat Events on Cooling Energy Demand in Hong Kong. Renew. Energy 2019, 142, 73–84. [Google Scholar] [CrossRef]
  43. Ralston Fonseca, F.; Jaramillo, P.; Bergés, M.; Severnini, E. Seasonal Effects of Climate Change on Intra-Day Electricity Demand Patterns. Clim. Change 2019, 154, 435–451. [Google Scholar] [CrossRef]
  44. Hekkenberg, M.; Moll, H.C.; Uiterkamp, A.J.M.S. Dynamic Temperature Dependence Patterns in Future Energy Demand Models in the Context of Climate Change. Energy 2009, 34, 1797–1806. [Google Scholar] [CrossRef]
  45. Romitti, Y.; Sue Wing, I. Heterogeneous Climate Change Impacts on Electricity Demand in World Cities circa Mid-Century. Sci. Rep. 2022, 12, 4280. [Google Scholar] [CrossRef]
  46. Añel, J.; Fernández-González, M.; Labandeira, X.; López-Otero, X.; De La Torre, L. Impact of Cold Waves and Heat Waves on the Energy Production Sector. Atmosphere 2017, 8, 209. [Google Scholar] [CrossRef]
  47. Forzieri, G.; Bianchi, A.; Silva, F.B.E.; Marin Herrera, M.A.; Leblois, A.; Lavalle, C.; Aerts, J.C.J.H.; Feyen, L. Escalating Impacts of Climate Extremes on Critical Infrastructures in Europe. Glob. Environ. Change 2018, 48, 97–107. [Google Scholar] [CrossRef]
  48. Linnerud, K.; Mideksa, T.K.; Eskeland, G.S. The Impact of Climate Change on Nuclear Power Supply. Energy J. 2011, 32, 149–168. [Google Scholar] [CrossRef]
  49. Marshall, A.; Upadhyay, S. Hydroclimate Risks to the Western US Electric Grid under a Warming Climate. Curr. Sustain./Renew. Energy Rep. 2024, 11, 68–76. [Google Scholar] [CrossRef]
  50. You, J.; Yin, F.; Gao, L. Escalating Wind Power Shortages during Heatwaves. Commun. Earth Environ. 2025, 6, 245. [Google Scholar] [CrossRef]
  51. Cawthorne, D.; De Queiroz, A.R.; Eshraghi, H.; Sankarasubramanian, A.; DeCarolis, J.F. The Role of Temperature Variability on Seasonal Electricity Demand in the Southern US. Front. Sustain. Cities 2021, 3, 644789. [Google Scholar] [CrossRef]
  52. Lam, C.K.C.; He, Q.; Cheng, K.; Fan, P.Y.; Chun, K.P.; Choi, B.; Mah, D.N.; Cheung, D.M.; Lo, K.; Yetemen, O. Impact of Climate Change and Socioeconomic Factors on Domestic Energy Consumption: The Case of Hong Kong and Singapore. Energy Rep. 2022, 8, 12886–12904. [Google Scholar] [CrossRef]
  53. United Nations. World Urbanization Prospects 2018: Highlights; Department of Economic and Social Affairs, Population Division: New York, NY, USA, 2019. [Google Scholar]
  54. Choi, Y.-W.; Campbell, D.J.; Aldridge, J.C.; Eltahir, E.A.B. Near-Term Regional Climate Change over Bangladesh. Clim. Dyn. 2021, 57, 3055–3073. [Google Scholar] [CrossRef]
  55. Jihan, M.A.T.; Popy, S.; Kayes, S.; Rasul, G.; Maowa, A.S.; Rahman, M.M. Climate Change Scenario in Bangladesh: Historical Data Analysis and Future Projection Based on CMIP6 Model. Sci. Rep. 2025, 15, 7856. [Google Scholar] [CrossRef]
  56. Paul, A.R.; Maity, R. Future Projection of Climate Extremes across Contiguous Northeast India and Bangladesh. Sci. Rep. 2023, 13, 15616. [Google Scholar] [CrossRef]
  57. Kirby, M.; Mainuddin, M. The Impact of Climate Change, Population Growth and Development on Sustainable Water Security in Bangladesh to 2100. Sci. Rep. 2022, 12, 22344. [Google Scholar] [CrossRef]
  58. Shaman, J.; Cane, M.; Kaplan, A. The Relationship between Tibetan Snow Depth, ENSO, River Discharge and the Monsoons of Bangladesh. Int. J. Remote Sens. 2005, 26, 3735–3748. [Google Scholar] [CrossRef]
  59. Alam, M.M.; Mahtab, A.S.M.; Ahmed, M.R.; Hassan, Q.K. Characterizing Cold Days and Spells and Their Relationship with Cold-Related Mortality in Bangladesh. Sensors 2023, 23, 2832. [Google Scholar] [CrossRef]
  60. Imran, H.M.; Kala, J.; Uddin, S.; Saiful Islam, A.K.M.; Acharya, N. Spatiotemporal Analysis of Temperature and Precipitation Extremes over Bangladesh Using a Novel Gridded Observational Dataset. Weather Clim. Extrem. 2023, 39, 100544. [Google Scholar] [CrossRef]
  61. Wu, Y.; Yang, J.; Zhang, Z.; Das, L.C.; Crabbe, M.J.C. High-Resolution Temperature Evolution Maps of Bangladesh via Data-Driven Learning. Atmosphere 2024, 15, 385. [Google Scholar] [CrossRef]
  62. Shahid, S. Rainfall Variability and the Trends of Wet and Dry Periods in Bangladesh. Int. J. Climatol. 2010, 30, 2299–2313. [Google Scholar] [CrossRef]
  63. Abdullah, A.Y.M.; Masrur, A.; Adnan, M.S.G.; Baky, M.A.A.; Hassan, Q.K.; Dewan, A. Spatio-Temporal Patterns of Land Use/Land Cover Change in the Heterogeneous Coastal Region of Bangladesh between 1990 and 2017. Remote Sens. 2019, 11, 790. [Google Scholar] [CrossRef]
  64. Alam, S. Fixing Bangladesh’s Power Sector; Institute of Energy Economics and Financial Analysis: Cleveland, OH, USA, 2024. [Google Scholar]
  65. Das, N.K.; Chakrabartty, J.; Dey, M.; Gupta, A.K.S.; Matin, M.A. Present Energy Scenario and Future Energy Mix of Bangladesh. Energy Strategy Rev. 2020, 32, 100576. [Google Scholar] [CrossRef]
  66. Alam, M.M.; Mahtab, A.S.M.; Ahmed, M.R.; Hassan, Q.K. A Percentile Method to Determine Cold Days and Spells in Bangladesh. Appl. Sci. 2023, 13, 7030. [Google Scholar] [CrossRef]
  67. World Meteorological Organization. Guidelines on the Definition and Characterization of Extreme Weather and Climate Events; World Meteorological Organization: Geneva, Switzerland, 2023; ISBN 978-92-63-11310-8. [Google Scholar]
  68. Aryal, S.; Babel, M.S.; Gupta, A.; Farjad, B.; Khadka, D.; Hassan, Q.K. Assessment of Hydrological Baseline Condition and Its Alteration in Athabasca River Basin, Canada. J. Hydrol. Reg. Stud. 2024, 53, 101805. [Google Scholar] [CrossRef]
  69. Dastour, H.; Mahbub Alam, M.; Dewan, A.; Hassan, Q.K. Spatiotemporal Analysis of Land Surface Temperature and Wind Dynamics during Winter in Bangladesh. Results Eng. 2025, 27, 106498. [Google Scholar] [CrossRef]
  70. Subramanian, A.; Nagarajan, A.M.; Vinod, S.; Chakraborty, S.; Sivagami, K.; Theodore, T.; Sathyanarayanan, S.S.; Tamizhdurai, P.; Mangesh, V.L. Long-Term Impacts of Climate Change on Coastal and Transitional Eco-Systems in India: An Overview of Its Current Status, Future Projections, Solutions, and Policies. RSC Adv. 2023, 13, 12204–12228. [Google Scholar] [CrossRef]
  71. Ahmed, A.Y.; Ali, A.M.; Ahmed, N. Temporal Dynamics of Leaf Area Index and Land Surface Temperature Correlation Using Sentinel-2 and Landsat OLI Data. Environ. Syst. Res. 2024, 13, 43. [Google Scholar] [CrossRef]
  72. Imran, H.M.; Hossain, A.; Shammas, M.I.; Das, M.K.; Islam, M.R.; Rahman, K.; Almazroui, M. Land Surface Temperature and Human Thermal Comfort Responses to Land Use Dynamics in Chittagong City of Bangladesh. Geomat. Nat. Hazards Risk 2022, 13, 2283–2312. [Google Scholar] [CrossRef]
  73. Tabassum, A.; Park, K.; Seo, J.M.; Han, J.-Y.; Baik, J.-J. Characteristics of the Urban Heat Island in Dhaka, Bangladesh, and Its Interaction with Heat Waves. Asia-Pac. J. Atmos. Sci. 2024, 60, 479–493. [Google Scholar] [CrossRef]
  74. Mainuddin, M.; Maniruzzaman, M.; Alam, M.M.; Mojid, M.A.; Schmidt, E.J.; Islam, M.T.; Scobie, M. Water Usage and Productivity of Boro Rice at the Field Level and Their Impacts on the Sustainable Groundwater Irrigation in the North-West Bangladesh. Agric. Water Manag. 2020, 240, 106294. [Google Scholar] [CrossRef]
  75. Arshad, S.; Beyer, R.C.M. Tracking Economic Fluctuations with Electricity Consumption in Bangladesh. Energy Econ. 2023, 123, 106740. [Google Scholar] [CrossRef]
  76. Istiaque, A.; Khan, S.I. Impact of Ambient Temperature on Electricity Demand of Dhaka City of Bangladesh. Energy Power Eng. 2018, 10, 319–331. [Google Scholar] [CrossRef]
  77. Rahman, S.M.T.; Kabir, A. Factors Influencing Location Choice and Cluster Pattern of Manufacturing Small and Medium Enterprises in Cities: Evidence from Khulna City of Bangladesh. J. Glob. Entrepr. Res. 2019, 9, 61. [Google Scholar] [CrossRef]
  78. Ahmed, T.; Muttaqi, K.M.; Agalgaonkar, A.P. Climate Change Impacts on Electricity Demand in the State of New South Wales, Australia. Appl. Energy 2012, 98, 376–383. [Google Scholar] [CrossRef]
  79. Asian Development Bank. GDP Growth in Asia and the Pacific, Asian Development Outlook (ADO). 2025. Available online: https://data.adb.org/dataset/gdp-growth-asia-and-pacific-asian-development-outlook (accessed on 3 October 2025).
Earth 06 00127 g001
Figure 1. Study area map showing six sub-regions, districts within them, and their corresponding meteorological station in southwestern regions of Bangladesh.
Figure 1. Study area map showing six sub-regions, districts within them, and their corresponding meteorological station in southwestern regions of Bangladesh.
Earth 06 00127 g001
Earth 06 00127 g002
Figure 2. Schematic diagram of the overall methods, illustrating the identification of extreme/normal temperature days, their trends, and influence on electricity demand.
Figure 2. Schematic diagram of the overall methods, illustrating the identification of extreme/normal temperature days, their trends, and influence on electricity demand.
Earth 06 00127 g002
Earth 06 00127 g003
Figure 3. Daily electricity demand (MWh) as a function of daily mean temperature (°C) for six sub-regions: (a) Barishal, (b) Faridpur, (c) Jashore, (d) Khulna, (e) Kushtia, and (f) Patuakhali. Points represent observed daily values, with best-fit curves shown for the early reference years (2020–2021; blue), the full record (2020–2024; black), and the most recent years (2023–2024; orange).
Figure 3. Daily electricity demand (MWh) as a function of daily mean temperature (°C) for six sub-regions: (a) Barishal, (b) Faridpur, (c) Jashore, (d) Khulna, (e) Kushtia, and (f) Patuakhali. Points represent observed daily values, with best-fit curves shown for the early reference years (2020–2021; blue), the full record (2020–2024; black), and the most recent years (2023–2024; orange).
Earth 06 00127 g003
Earth 06 00127 g004
Figure 4. Linear regression relationships between daily electricity demand (MWh) and daily maximum temperature (°C) during hot days identified by TX90, TX95, and TX99 thresholds across six sub-regions: (a) Barishal, (b) Faridpur, (c) Jashore, (d) Khulna, (e) Kushtia, and (f) Patuakhali in southwestern Bangladesh. Each line corresponds to a separate regression fit for one threshold level. Regression equations and associated correlation coefficients (r) are reported within each panel.
Figure 4. Linear regression relationships between daily electricity demand (MWh) and daily maximum temperature (°C) during hot days identified by TX90, TX95, and TX99 thresholds across six sub-regions: (a) Barishal, (b) Faridpur, (c) Jashore, (d) Khulna, (e) Kushtia, and (f) Patuakhali in southwestern Bangladesh. Each line corresponds to a separate regression fit for one threshold level. Regression equations and associated correlation coefficients (r) are reported within each panel.
Earth 06 00127 g004
Earth 06 00127 g005
Figure 5. Linear regression relationships between daily electricity demand (MWh) and daily minimum temperature (°C) during cold days identified by TN10, TN5, and TN1 thresholds across six sub-regions: (a) Barishal, (b) Faridpur, (c) Jashore, (d) Khulna, (e) Kushtia, and (f) Patuakhali in southwestern Bangladesh. Each line corresponds to a separate regression fit for one threshold level. Regression equations and associated correlation coefficients (r) are reported within each panel.
Figure 5. Linear regression relationships between daily electricity demand (MWh) and daily minimum temperature (°C) during cold days identified by TN10, TN5, and TN1 thresholds across six sub-regions: (a) Barishal, (b) Faridpur, (c) Jashore, (d) Khulna, (e) Kushtia, and (f) Patuakhali in southwestern Bangladesh. Each line corresponds to a separate regression fit for one threshold level. Regression equations and associated correlation coefficients (r) are reported within each panel.
Earth 06 00127 g005
Earth 06 00127 g006
Figure 6. Linear regression relationships between daily electricity demand (MWh) and daily maximum temperature (°C) during hot days identified by TX90 threshold in 2020–2021 and 2023–2024 across six sub-regions: (a) Barishal, (b) Faridpur, (c) Jashore, (d) Khulna, (e) Kushtia, and (f) Patuakhali in southwestern Bangladesh. Each line represents a separate linear fit for the respective period. Regression equations and associated correlation coefficients (r) are reported within each panel.
Figure 6. Linear regression relationships between daily electricity demand (MWh) and daily maximum temperature (°C) during hot days identified by TX90 threshold in 2020–2021 and 2023–2024 across six sub-regions: (a) Barishal, (b) Faridpur, (c) Jashore, (d) Khulna, (e) Kushtia, and (f) Patuakhali in southwestern Bangladesh. Each line represents a separate linear fit for the respective period. Regression equations and associated correlation coefficients (r) are reported within each panel.
Earth 06 00127 g006
Earth 06 00127 g007
Figure 7. Linear regression relationships between daily electricity demand (MWh) and daily minimum temperature (°C) during cold days identified by TN10 threshold in 2020–2021 and 2023–2024 across six sub-regions: (a) Barishal, (b) Faridpur, (c) Jashore, (d) Khulna, (e) Kushtia, and (f) Patuakhali in southwestern Bangladesh. Each line represents a separate linear fit for the respective period. Regression equations and associated correlation coefficients (r) are reported within each panel.
Figure 7. Linear regression relationships between daily electricity demand (MWh) and daily minimum temperature (°C) during cold days identified by TN10 threshold in 2020–2021 and 2023–2024 across six sub-regions: (a) Barishal, (b) Faridpur, (c) Jashore, (d) Khulna, (e) Kushtia, and (f) Patuakhali in southwestern Bangladesh. Each line represents a separate linear fit for the respective period. Regression equations and associated correlation coefficients (r) are reported within each panel.
Earth 06 00127 g007
Table 1. Sub-zonal land area, meteorological stations including coordinates, elevation, area, density, and population in 2022 of the southwestern regions of Bangladesh.
Table 1. Sub-zonal land area, meteorological stations including coordinates, elevation, area, density, and population in 2022 of the southwestern regions of Bangladesh.
Long. ELat. NElev. (m)PopulationArea (km2)Density/km2
Barishal90.3322.752.14,429,8044770929
Faridpur89.8523.608.17,235,33869131047
Jashore89.1723.186.17,095,2188431842
Khulna89.5322.782.14,226,4648353506
Patuakhali90.3322.354.04,670,2988455552
Kushtia (Chuadanga)89.1223.8120.05,860,41854901067
Table 2. The threshold of temperature of hot days and cold days based on different percentiles of daily maximum temperature, daily minimum temperature, and daily average temperature.
Table 2. The threshold of temperature of hot days and cold days based on different percentiles of daily maximum temperature, daily minimum temperature, and daily average temperature.
StationBaseline PeriodHot Day Threshold (°C)Cold Day Threshold (°C)
TX90TX95TX99TN10TN5TN1
Barishal1961–199033.934.736.112.411.09.0
Chuadanga 1991–202036.537.740.011.49.57.2
Faridpur1961–199034.536.037.412.211.08.9
Jashore1961–199036.037.239.611.510.07.5
Patuakhali1981–201034.335.036.414.312.810.7
Khulna1961–199035.536.437.713.711.99.6
Note: TX90, TX95, and TX99 represent 90th, 95th, and 99th percentile of daily maximum temperature during baseline period while TN10, TN5, and TN1 represent 10th, 5th, and 1st percentile of daily minimum temperature during baseline period.
Table 3. Trend in annual number of (a) hot days, and (b) cold days based on different thresholds during 1961–2024 and 1991–2024 for the six locations using the Mann–Kendall test. The Sen’s slope (days/yr) has been provided corresponding to each threshold, where *, ** indicate the 95% and 99% confidence levels, respectively.
Table 3. Trend in annual number of (a) hot days, and (b) cold days based on different thresholds during 1961–2024 and 1991–2024 for the six locations using the Mann–Kendall test. The Sen’s slope (days/yr) has been provided corresponding to each threshold, where *, ** indicate the 95% and 99% confidence levels, respectively.
PeriodsThreshold(a) Trends in Annual Frequency of Hot Days (Days/Year)
KhulnaJashoreFaridpurBarishalPatuakhali 1Chuadanga
1961–2024TX900.57 **0.47 **0.96 **1.13 **1.78 **NA
TX950.26 **0.21 *0.22 *0.67 **1.26 **NA
TX990.03 **0.000.05 **0.09 *0.26 **NA
1991–2024TX900.84 **0.59 *1.82 **1.62 **2.00**−0.04
TX950.71 **0.300.451.24 **1.54 **−0.19
TX990.18 **0.000.000.30 **0.35 **0.00
(b) Trends in Annual Frequency of Cold Days (Days/Year)
1961–2024TN100.19−0.06−0.31 **0.000.21NA
TN50.04−0.04−0.100.060.23 *NA
TN10.00−0.020.000.020.03NA
1991–2024TN10−0.72 **−0.14−0.05−0.33−0.160.16
TN5−0.63 **−0.13−0.14−0.22−0.090.07
TN1−0.21 **0.000.000.00−0.050.00
1 Long-term trend analysis at Patuakhali was carried out for 1981 to 2024.
Table 4. Daily electricity demand (MWh) expressed as average and standard deviation across hot, normal, and cold days, classified using different temperature thresholds (TX90/TN10, TX95/TN5, TX99/TN1) for each sub-region.
Table 4. Daily electricity demand (MWh) expressed as average and standard deviation across hot, normal, and cold days, classified using different temperature thresholds (TX90/TN10, TX95/TN5, TX99/TN1) for each sub-region.
ThresholdDayBarishalKushtiaFaridpurJashoreKhulnaPatuakhali
TX90/TN10Hot80.7 ± 12.9104.0 ± 18.192.1 ± 15.073.8 ± 11.2149.1 ± 19.930.1 ± 4.7
Normal67.8 ± 13.385.0 ± 17.173.5 ± 14.558.0 ± 12.0123.7 ± 20.625.4 ± 5.3
Cold52.0 ± 5.566.7 ± 5.759.8 ± 4.442.8 ± 3.299.5 ± 7.819.1 ± 1.2
TX95/TN5Hot82.0 ± 13.9108.1 ± 16.594.9 ± 17.275.0 ± 11.1153.6 ± 19.930.7 ± 4.6
Normal68.5 ± 13.785.3 ± 17.976.1 ± 15.857.7 ± 12.3123.6 ± 21.525.3 ± 5.4
Cold51.8 ± 5.865.6 ± 6.360.1 ± 4.842.7 ± 2.7100.2 ± 6.019.1 ± 1.3
TX99/TN1Hot87.9 ± 14.9114.8 ± 10.7100.9 ± 15.279.9 ± 8.1161.1 ± 15.732.2 ± 4.6
Normal69.1 ± 14.285.1 ± 18.376.7 ± 16.358.1 ± 12.9124.5 ± 22.325.6 ± 5.6
Cold52.9 ± 6.767.7 ± 6.160.0 ± 5.443.1 ± 0.4101.3 ± 3.919.5 ± 1.3
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Alam, M.M.; Aryal, S.; Hassan, Q.K. Shifting Electricity Demand Under Temperature Extremes in Bangladesh. Earth 2025, 6, 127. https://doi.org/10.3390/earth6040127

AMA Style

Alam MM, Aryal S, Hassan QK. Shifting Electricity Demand Under Temperature Extremes in Bangladesh. Earth. 2025; 6(4):127. https://doi.org/10.3390/earth6040127

Chicago/Turabian Style

Alam, Md. Mahbub, Sharad Aryal, and Quazi K. Hassan. 2025. "Shifting Electricity Demand Under Temperature Extremes in Bangladesh" Earth 6, no. 4: 127. https://doi.org/10.3390/earth6040127

APA Style

Alam, M. M., Aryal, S., & Hassan, Q. K. (2025). Shifting Electricity Demand Under Temperature Extremes in Bangladesh. Earth, 6(4), 127. https://doi.org/10.3390/earth6040127

Article Metrics

Back to TopTop