In 2023, the Philippines recorded an annual maximum temperature of **** degrees Celsius and an annual minimum temperature of **** degrees Celsius. On average, the country's temperature for the year was **** degrees Celsius.

Temperature in Philippines increased to 26.91 celsius in 2023 from 26.61 celsius in 2022. This dataset includes a chart with historical data for Philippines Average Temperature.

In 2023, the highest monthly average temperature in the Philippines was ** degrees Celsius, recorded in December at the CLSU station in Nueva Ecija. In contrast, the lowest monthly average temperature was **** degrees Celsius, recorded in January in Baguio City, Benguet.

According to a survey conducted by Ipsos, about 79 percent of the respondents in the Philippines believed that the average global temperature would increase in 2020. Among all the surveyed respondents, only 54 percent in Saudi Arabia believed that it would occur in 2020.

Philippines Heating Degree Days data was reported at 1.690 Degrees Celsius in 2020. This records an increase from the previous number of 1.080 Degrees Celsius for 2019. Philippines Heating Degree Days data is updated yearly, averaging 3.370 Degrees Celsius from Dec 1970 (Median) to 2020, with 51 observations. The data reached an all-time high of 8.380 Degrees Celsius in 1981 and a record low of 0.290 Degrees Celsius in 2012. Philippines Heating Degree Days data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Environmental: Climate Risk. A heating degree day (HDD) is a measurement designed to track energy use. It is the number of degrees that a day's average temperature is below 18°C (65°F). Daily degree days are accumulated to obtain annual values.;World Bank, Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org;;

Philippines Cooling Degree Days data was reported at 5,254.980 Degrees Celsius in 2020. This records an increase from the previous number of 5,184.350 Degrees Celsius for 2019. Philippines Cooling Degree Days data is updated yearly, averaging 4,722.850 Degrees Celsius from Dec 1970 (Median) to 2020, with 51 observations. The data reached an all-time high of 5,303.640 Degrees Celsius in 2016 and a record low of 4,293.460 Degrees Celsius in 1971. Philippines Cooling Degree Days data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Environmental: Climate Risk. A cooling degree day (CDD) is a measurement designed to track energy use. It is the number of degrees that a day's average temperature is above 18°C (65°F). Daily degree days are accumulated to obtain annual values.;World Bank, Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org;;

Philippines PH: CO2 Emissions from Electricity and Heat Production: % of Total Fuel Combustion data was reported at 50.162 % in 2014. This records an increase from the previous number of 49.738 % for 2013. Philippines PH: CO2 Emissions from Electricity and Heat Production: % of Total Fuel Combustion data is updated yearly, averaging 34.089 % from Dec 1971 (Median) to 2014, with 44 observations. The data reached an all-time high of 50.162 % in 2014 and a record low of 25.833 % in 1972. Philippines PH: CO2 Emissions from Electricity and Heat Production: % of Total Fuel Combustion data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Environment: Pollution. CO2 emissions from electricity and heat production is the sum of three IEA categories of CO2 emissions: (1) Main Activity Producer Electricity and Heat which contains the sum of emissions from main activity producer electricity generation, combined heat and power generation and heat plants. Main activity producers (formerly known as public utilities) are defined as those undertakings whose primary activity is to supply the public. They may be publicly or privately owned. This corresponds to IPCC Source/Sink Category 1 A 1 a. For the CO2 emissions from fuel combustion (summary) file, emissions from own on-site use of fuel in power plants (EPOWERPLT) are also included. (2) Unallocated Autoproducers which contains the emissions from the generation of electricity and/or heat by autoproducers. Autoproducers are defined as undertakings that generate electricity and/or heat, wholly or partly for their own use as an activity which supports their primary activity. They may be privately or publicly owned. In the 1996 IPCC Guidelines, these emissions would normally be distributed between industry, transport and 'other' sectors. (3) Other Energy Industries contains emissions from fuel combusted in petroleum refineries, for the manufacture of solid fuels, coal mining, oil and gas extraction and other energy-producing industries. This corresponds to the IPCC Source/Sink Categories 1 A 1 b and 1 A 1 c. According to the 1996 IPCC Guidelines, emissions from coke inputs to blast furnaces can either be counted here or in the Industrial Processes source/sink category. Within detailed sectoral calculations, certain non-energy processes can be distinguished. In the reduction of iron in a blast furnace through the combustion of coke, the primary purpose of the coke oxidation is to produce pig iron and the emissions can be considered as an industrial process. Care must be taken not to double count these emissions in both Energy and Industrial Processes. In the IEA estimations, these emissions have been included in this category.; ; IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/; Weighted average; Restricted use: Please contact the International Energy Agency for third-party use of these data.

Philippines PH: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data was reported at 0.806 % in 2009. Philippines PH: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data is updated yearly, averaging 0.806 % from Dec 2009 (Median) to 2009, with 1 observations. Philippines PH: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank: Land Use, Protected Areas and National Wealth. Droughts, floods and extreme temperatures is the annual average percentage of the population that is affected by natural disasters classified as either droughts, floods, or extreme temperature events. A drought is an extended period of time characterized by a deficiency in a region's water supply that is the result of constantly below average precipitation. A drought can lead to losses to agriculture, affect inland navigation and hydropower plants, and cause a lack of drinking water and famine. A flood is a significant rise of water level in a stream, lake, reservoir or coastal region. Extreme temperature events are either cold waves or heat waves. A cold wave can be both a prolonged period of excessively cold weather and the sudden invasion of very cold air over a large area. Along with frost it can cause damage to agriculture, infrastructure, and property. A heat wave is a prolonged period of excessively hot and sometimes also humid weather relative to normal climate patterns of a certain region. Population affected is the number of people injured, left homeless or requiring immediate assistance during a period of emergency resulting from a natural disaster; it can also include displaced or evacuated people. Average percentage of population affected is calculated by dividing the sum of total affected for the period stated by the sum of the annual population figures for the period stated.; ; EM-DAT: The OFDA/CRED International Disaster Database: www.emdat.be, Université Catholique de Louvain, Brussels (Belgium), World Bank.; ;

The dataset provides microclimate time series for climate loggers in the canopy, the understory, and inside Asplenium bird's nest ferns along an elevation gradient spanning 900 m to 1900 m on Mt. Banahaw, the Philippines. Trait characteristics were additionally collected for the ferns in which microclimate were monitored. Methods The data were collected along an elevation gradient spanning 900 m to 1900 m a.s.l. on Mount Banahaw in southern Luzon, the Philippines. The gradient is characterized by dipterocarp and montane rainforest from 900 m to 1700 m and mossy and Pinus forest above 1700 m a.s.l. Microclimate Data We monitored temperature in the understory, canopy, and inside BNFs every 200 m in elevation using temperature data loggers (Maxim Hygrochron ibutton Model DS1923; http://www.maxim-ic.com/). We placed a pair of temperature loggers in four to five trees per elevation. In each tree, loggers were placed in the upper canopy, which varied across elevations and transects from an average of 11.75 m to 18.75 m, and directly below, suspended 1 m above the ground (hereafter ‘understory’). Paired canopy-understory loggers were separated by a minimum of 100 m to reduce spatial autocorrelation. Along two 50 m transects at each elevation from 900 m to 1700 m, we quantified the abundance and the vertical distribution of BNFs by counting all BNFs that were visible from the transect and recording their height above ground using a laser distance meter (Leica Geosystems, Leica Disto D2; http://www.leica-geosystems.ca). We then calculated relative height of each BNF by dividing the height above ground by the average canopy height of the given elevation and transect. Average canopy height was obtained by surveying a total of 59 canopy trees for heights (14 trees at 900 m, five at 1100 m, 13 at 1300 m, five at 1500 m, 11 at 1700 m, five at 1900 m and six at 2100 m elevation). We randomly selected a subset of 50 BNFs along one of the surveyed transects at each elevation for which we monitored microclimates. Data loggers were placed under detritus at the center of each fern. For these BNFs, we collected additional data on their habitat and morphological characteristics, including canopy cover (directly above fern), aspect, average length of the five longest fronds, fern size, and number of leaves. Fern size was measured as the width multiplied by the height of the humus-root ball. Data loggers recorded temperature at 15–20-minute intervals from May through September 2011 for canopy and understory microhabitats and June through August 2011 for BNF microhabitats. Habitat and morphological measurements and microclimate data were obtained for 50 BNFs. Due to technical malfunctions of data loggers, elevations were not represented equally (900 m: n = 8; 1100 m: n = 16; 1300 m: n = 15; 1500 m: n = 10; 1700 m: n = 1). Precipitation data Precipitation data was collected at 1100 m a.s.l.

BackgroundDespite an unknown cause, Kawasaki disease (KD) is currently the primary leading cause of acquired heart disease in developed countries in children and has been increasing in recent years. Research efforts have explored environmental factors related to KD, but they are still unclear especially in the tropics. We aimed to describe the incidence of KD in children, assess its seasonality, and determine its association with ambient air temperature in the National Capital Region (NCR), Philippines from January 2009 to December 2019.MethodsMonthly number of KD cases from the Philippine Pediatric Society (PPS) disease registry was collected to determine the incidence of KD. A generalized linear model (GLM) with quasi-Poisson regression was utilized to assess the seasonality of KD and determine its association with ambient air temperature after adjusting for the relevant confounders.ResultsThe majority of KD cases (68.52%) occurred in children less than five years old, with incidence rates ranging from 14.98 to 23.20 cases per 100,000 population, and a male-to-female ratio of 1.43:1. Seasonal variation followed a unimodal shape with a rate ratio of 1.13 from the average, peaking in March and reaching the lowest in September. After adjusting for seasonality and long-term trend, every one-degree Celsius increase in the monthly mean temperature significantly increased the risk of developing KD by 8.28% (95% CI: 2.12%, 14.80%). Season-specific analysis revealed a positive association during the dry season (RR: 1.06, 95% CI: 1.01, 1.11), whereas no evidence of association was found during the wet season (RR: 1.10, 95% CI: 0.95, 1.27).ConclusionWe have presented the incidence of KD in the Philippines which is relatively varied from its neighboring countries. The unimodal seasonality of KD and its linear association with temperature, independent of season and secular trend, especially during dry season, may provide insights into its etiology and may support enhanced KD detection efforts in the country.

Philippines Energy Balance: Primary: Natural Gas: Electricity and Heat Generation data was reported at 405.000 Cub m mn in Feb 2025. This records a decrease from the previous number of 420.000 Cub m mn for Jan 2025. Philippines Energy Balance: Primary: Natural Gas: Electricity and Heat Generation data is updated monthly, averaging 305.500 Cub m mn from Jan 2009 (Median) to Feb 2025, with 194 observations. The data reached an all-time high of 470.000 Cub m mn in May 2024 and a record low of 45.000 Cub m mn in Oct 2021. Philippines Energy Balance: Primary: Natural Gas: Electricity and Heat Generation data remains active status in CEIC and is reported by Joint Organisations Data Initiative. The data is categorized under Global Database’s Philippines – Table PH.JODI.WDB: Energy Balance: Gas. Natural Gas Natural gas is defined as a mixture of gaseous hydrocarbons, primarily methane, but generally also including ethane, propane and higher hydrocarbons in much smaller amounts and some non-combustible gases such as nitrogen and carbon dioxide. It includes both non-associated gas and associated gas. Colliery gas, coal seam gas and shale gas are included while manufactured gas and biogas are excluded except when blended with natural gas for final consumption. Natural gas liquids are excluded.; Of which: Electricity and Heat Generation This covers the deliveries of natural gas for the generation of electricity and heat in power plants. Both main-activity and autoproducer plants are included.

Seasonal rainfall in the Philippines is known to be modulated by ENSO phenomenon, with El Nino frequently contributing to reduced rainfall and drought while La Nina resulting in excessive rainfalls, floods and more intense typhoons. The alterations in rainfall patterns can have considerable feedback on solar radiation, air temperature, and soil moisture which can affect the ecosystem CO2 exchange. In this paper, we assessed the effects of the ENSO events (2008-mid 2010) on the seasonal climate conditions and determined how it affected the gross primary production (GPP), ecosystem respiration (Re), and net ecosystem production (NEP) of two contrasting rice environments: flooded and non-flooded. The 2008 dry season (DS) was under a La Nina event while the 2008 wet season (WS) was a neutral one with strong tropical cyclones associated during the wet season. The 2009DS was also La Ninna while the 2009WS was El Nino; however, the northern part of the Philippines experienced strong tropical cyclones. The 2010DS was under an El Nino event. The La Nina in 2008DS resulted in about 15% lower solar radiation (SR), 0.3 degre es Centigrade lower air temperature (Ta) and 60% higher precipitation compared to the 28-year climate normal patterns. Both flooded and non-flooded rice fields had lower NEP in 2008 DS (164 and 14 g C/m2, respectively) than in 2008 WS (295 and 82 g C/m2, respectively) because the climate anomaly resulted in SR-driven decrease in GPP. The La Nina in 2009DS even resulted in 0.2 deg C lower air temperature and 40% more precipitation than the 2008DS La Nina. This cooler temperature resulted in lower Re in flooded rice fields while the higher precipitation resulted in higher GPP in non-flooded fields since the climate was favorable for the growth of the aerobic rice, the ratoon crops and the weeds. This climate anomaly benefitted both flooded and non-flooded rice fields by increasing NEP (351 and 218 g C/m2, respectively). However, NEP decreased in 2009WS in both flooded and non-flooded rice fields (225 and 39 g C/m2, respectively) due to the devastating effects of the strong tropical cyclones that hit the northern part of the Philippines. On the other hand, the El Nino event during 2010DS resulted in about 6% higher solar radiation, 0.4 degrees Centigrade higher air temperature and 67% lower precipitation than the 28-year climate normal pattern. The NEP of flooded and non-flooded rice fields were closely similar at 187 and 174 g C/m2, respectively. This climate anomaly resulted in Ta - driven increase in Re, as well as vapor pressure deficit (VPD) - driven decrease in GPP in flooded rice fields. The GPP and Re in non-flooded rice fields were less sensitive to higher VPD and higher Ta, respectively. It appears that the net ecosystem CO2 exchange in non-flooded rice field was less sensitive to an El Nino event.

Japan AE: Philippines: AS: Hot Spring, Relaxation data was reported at 7,324.187 JPY in Mar 2018. Japan AE: Philippines: AS: Hot Spring, Relaxation data is updated quarterly, averaging 7,324.187 JPY from Mar 2018 (Median) to Mar 2018, with 1 observations. Japan AE: Philippines: AS: Hot Spring, Relaxation data remains active status in CEIC and is reported by Ministry of Land, Infrastructure, Transport and Tourism. The data is categorized under Global Database’s Japan – Table JP.Q028: Tourism and Leisure: Average Expenditure per Purchaser by Nationality.

PH: Annual Surface Temperature: Change Since 1951 1980 data was reported at 1.179 Number in 2021. This records a decrease from the previous number of 1.316 Number for 2020. PH: Annual Surface Temperature: Change Since 1951 1980 data is updated yearly, averaging 0.686 Number from Dec 1990 (Median) to 2021, with 32 observations. The data reached an all-time high of 1.422 Number in 2016 and a record low of 0.368 Number in 1996. PH: Annual Surface Temperature: Change Since 1951 1980 data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Philippines – Table PH.OECD.GGI: Environmental: Climate Risk: Non OECD Member: Annual.

Philippines Percentage of Population Exposure to Hot Days data was reported at 20.300 % in 2021. This records a decrease from the previous number of 33.700 % for 2020. Philippines Percentage of Population Exposure to Hot Days data is updated yearly, averaging 25.350 % from Dec 1990 (Median) to 2021, with 32 observations. The data reached an all-time high of 33.700 % in 2020 and a record low of 4.500 % in 1999. Philippines Percentage of Population Exposure to Hot Days data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Philippines – Table PH.OECD.GGI: Social: Air Quality and Health: Non OECD Member: Annual.

The Philippine pacemaker market shrank slightly to $166M in 2024, which is down by -3.8% against the previous year. The market value increased at an average annual rate of +1.6% over the period from 2012 to 2024; the trend pattern remained consistent, with only minor fluctuations throughout the analyzed period. Over the period under review, the market reached the peak level at $182M in 2022; however, from 2023 to 2024, consumption remained at a lower figure.

Philippines RPI: Misc: Sanitary Plumbing, Heat and Lighting Fixtures data was reported at 1,191.620 1978=100 in Sep 2009. This records an increase from the previous number of 1,185.760 1978=100 for Aug 2009. Philippines RPI: Misc: Sanitary Plumbing, Heat and Lighting Fixtures data is updated monthly, averaging 799.660 1978=100 from Jan 1990 (Median) to Sep 2009, with 237 observations. The data reached an all-time high of 1,191.620 1978=100 in Sep 2009 and a record low of 490.770 1978=100 in Jun 1990. Philippines RPI: Misc: Sanitary Plumbing, Heat and Lighting Fixtures data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.I059: Retail Price Index: 1978=100: Metro Manila.

Global warming affects not only rice yield but also grain quality. A better understanding of the effects of climate factors on rice quality provides information for new breeding strategies to develop varieties of rice adapted to a changing world. Chalkiness is a key trait of physical quality, and along with head rice yield, is used to determine the price of rice in all markets. In the present study, we show that for every ∼1% decrease in chalkiness, an increase of ∼1% in head rice yield follows, illustrating the dual impact of chalk on amount of marketable rice and its value. Previous studies in controlled growing conditions report that chalkiness is associated with high temperature. From 1980–2009 at IRRI, Los Baños, the Philippines, annual minimum and mean temperatures, and diurnal variation changed significantly. The objective of this study was to determine how climate impacts chalkiness in field conditions over four wet and dry seasons. We show that low relative humidity and a high vapour pressure deficit in the dry season associate with low chalk and high head rice yield in spite of higher maximum temperature, but in the opposite conditions of the wet season, chalk is high and head rice yield is low. The data therefore suggest that transpirational cooling is a key factor affecting chalkiness and head rice yield, and global warming per se might not be the major factor that decreases the amount and quality of rice, but other climate factors in combination, that enable the crop to maintain a cool canopy.

Philippines Imports: SP: Articles Temp Imported data was reported at 0.047 kg mn in Apr 2018. This records a decrease from the previous number of 0.192 kg mn for Mar 2018. Philippines Imports: SP: Articles Temp Imported data is updated monthly, averaging 2.539 kg mn from Jan 2006 (Median) to Apr 2018, with 148 observations. The data reached an all-time high of 6.606 kg mn in Jul 2016 and a record low of 0.040 kg mn in Dec 2017. Philippines Imports: SP: Articles Temp Imported data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.JA017: Trade Statistics: Imports: By Commodity Group: 2004 PSCC: Volume.

Climate change is viewed as a major concern globally, with around 90 percent of respondents to a 2023 survey viewing it as a serious threat to humanity. developing nations often show the highest levels of concern, like in the Philippines where 96.7 percent of respondents acknowledge it as a serious threat. Rising emissions despite growing awareness Despite widespread acknowledgment of climate change, global greenhouse gas emissions continue to climb. In 2023, emissions reached a record high of 53 billion metric tons of carbon dioxide equivalent, marking a 60 percent increase since 1990. The power industry remains the largest contributor, responsible for 28 percent of global emissions. This ongoing rise in emissions has significant implications for global climate patterns and environmental stability. Temperature anomalies reflect warming trend In 2024, the global land and ocean surface temperature anomaly reached 1.29 degrees Celsius above the 20th-century average, the highest recorded deviation to date. This consistent pattern of positive temperature anomalies, observed since the 1980s, highlights the long-term warming effect of increased greenhouse gas accumulation in the atmosphere. The warmest years on record have all occurred within the past decade.