摘要: 氣候變化對人群健康的影響不斷加劇，亟待評價不適環(huán)境溫度對健康的不良影響，量化與溫度相關的死亡負擔和對應的健康經濟損失。本研究基于2013年1月1日至2015年12月31日中國272個主要城市的氣溫和人口死亡數據，采用時間序列方法建立溫度與死亡的暴露-反應關系。同時，收集2020年中國大陸364個城市的氣象、社會經濟和人口數據，進一步估算31個省、自治區(qū)、直轄市低溫和高溫暴露的歸因死亡人數和經濟損失。結果表明，環(huán)境溫度與死亡的暴露-反應關系近似呈反“J”型，環(huán)境低溫和高溫暴露均可引起死亡風險升高。2020年環(huán)境低溫和高溫暴露分別導致中國大陸84.24（95%置信區(qū)間（95%CI）：65.93—102.20）萬例和23.58（95%CI：14.69—32.17）萬例死亡；相應健康的經濟損失分別為17011.08（95%CI：13353.51—20597.72）億元和5097.35（95%CI：3179.66—6945.93）億元，共占國內生產總值（GDP）的2.18%。不適環(huán)境溫度暴露已對中國造成了較大的死亡負擔和健康經濟損失。未來還需加強行動應對氣候變化和不適環(huán)境溫度的健康威脅，因地制宜采取適應措施保護人群健康。

Abstract: With the increasing impact of climate change on public health, there is an urgent need to evaluate the detrimental effect of non-optimal ambient temperature on health and quantify the temperature-related mortality and corresponding economic losses. Based on the national database of weather conditions and mortality records in 272 main cities in China from 1 January 2013 to 31 December 2015, time-series analyses are conducted to estimate the exposure-response association between temperature and mortality. Besides, meteorological, socioeconomic, and demographic data for cities across China are collected to quantify the attributable deaths and corresponding economic losses due to low and high temperatures in 31 provinces, autonomous regions and municipalities of China. The exposure-response curve for the association between ambient temperature and mortality is J-shaped, with increased mortality risks for both low and high temperatures. As estimated, 842.4 (95%CI: 659.3—1022.0) thousand and 235.8 (95%CI: 146.9—321.7) thousand deaths are attributable to low and high temperatures in 2020 in China, respectively. The corresponding economic losses are 1701.11 (95%CI: 1335.35—2059.77) billion and 509.74 (95%CI: 317.97—694.59) billion Chinese yuan, respectively. The proportion of the overall economic loss to the gross domestic product (GDP) is 2.18%. Non-optimal ambient temperature exposure has led to substantial mortality and economic loss in China. It is necessary to strengthen actions to deal with the health threats of climate change and non-optimal ambient temperature, and local adaptation measures should be taken to protect public health in the future.

圖  1   中國環(huán)境溫度與總死亡的暴露-反應關系曲線 （a. 全國，b. 北方地區(qū)，c. 南方地區(qū)；陰影為95%置信區(qū)間）

Figure  1.   Cumulative exposure-response curves for relationships between ambient temperature and total mortality in China （a. Nationwide，b. Northern China，c. Southern China；shade is 95% confidential interval）

表  1   2020年中國31個省、自治區(qū)及直轄市的基本信息

Table  1   Basic information of 31 provinces，autonomous regions，and municipalities of China in 2020

變量人口
（萬）死亡率（‰）人均年收入
（萬元）生產總值
（億元）年均溫度
（℃）統計生命價值（萬元） 安徽6104.86.02.838680.616.7180.5北京2189.05.56.936102.613.8446.0重慶3208.97.63.125002.819.2198.0福建4161.46.13.743903.921.3238.9甘肅2500.56.82.09016.78.7130.6廣東12623.64.54.1110760.923.3263.5廣西5018.76.12.522156.721.9157.8貴州3857.97.02.217826.616.2140.0海南1011.76.12.85532.425.3179.2河北7463.86.12.736206.912.8174.3河南9941.26.82.554997.115.6159.4黑龍江3170.96.72.513698.54.3159.9湖北5744.87.12.843443.516.7179.1湖南6645.37.32.941781.517.7188.7吉林2399.26.92.612311.36.5165.4江蘇8477.37.04.3102719.016.8278.7江西4519.46.02.825691.518.7179.9遼寧4255.57.33.325115.010.1210.3內蒙古2402.85.73.117359.86.3202.3寧夏720.95.72.63920.69.9165.3青海592.86.12.43005.94.9154.4山東10164.57.53.373129.013.8211.2山西3490.45.92.517651.911.3161.9陜西3954.76.32.626181.913.0168.4上海2488.25.57.238700.617.8463.9四川8370.77.12.748598.815.8170.3天津1386.85.34.414083.713.8281.7西藏365.64.52.21902.77.6139.7新疆2590.54.52.413797.69.0153.2云南4722.26.22.324521.916.5149.6浙江6468.35.55.264613.318.5336.5全國141212.07.13.21015986.214.8206.7

表  2   中國不適環(huán)境溫度相關的相對危險度

Table  2   Relative risks associated with non-optimal ambient temperatures in China

變量城市數量（個）MMT （℃）極端低溫 （℃）極端高溫 （℃）相對危險度（均值及95%置信區(qū)間）極端低溫極端高溫 全國27222.8?1.429.01.67 （1.56—1.79）1.16 （1.11—1.20）北方11919.6?9.227.31.29 （1.19—1.40）1.11 （1.07—1.16）南方15323.7 4.730.31.40 （1.32—1.49）1.19 （1.11—1.27） 　注：MMT，最低死亡率溫度；極端低溫為溫度分布2.5%分位數；極端高溫為溫度分布97.5%分位數。

表  3   2020年全國31個省、自治區(qū)、直轄市的不適溫度相關的死亡歸因數 （均值及95%置信區(qū)間）

Table  3   Attributable number of deaths （mean value and the 95% confidential intervals） due to non-optimal ambient temperature in 31 provinces，autonomous regions and municipalities of China in 2020

變量歸因死亡數（萬人）歸因分數（%）低溫高溫低溫高溫匯總 安徽3.63 （2.91—4.34）1.04 （0.64—1.42）9.842.8112.65北京0.82 （0.56—1.07）0.35 （0.21—0.49）6.812.949.75重慶2.45 （2.02—2.87）0.87 （0.56—1.17）10.093.5813.67福建1.75 （1.43—2.06）1.04 （0.66—1.41）6.884.1010.98甘肅1.67 （1.19—2.15）0.13 （0.08—0.18）9.910.7810.69廣東2.74 （2.24—3.24）3.02 （1.91—4.08）4.875.3610.23廣西1.93 （1.59—2.28）1.39 （0.88—1.89）6.274.5210.79貴州3.87 （3.22—4.50）0.22 （0.14—0.30）14.430.8215.25海南0.12 （0.10—0.15）0.40 （0.25—0.54）1.976.448.41河北3.23 （2.23—4.23）1.07 （0.65—1.48）7.072.359.42河南4.06 （2.85—5.27）2.15 （1.31—2.96）5.963.169.12黑龍江2.58 （1.82—3.30）0.20 （0.12—0.27）12.060.9212.98湖北4.99 （4.14—5.80）1.07 （0.67—1.45）12.262.6214.88湖南5.52 （4.59—6.43）1.60 （1.01—2.16）11.423.3014.72吉林1.82 （1.28—2.34）0.21 （0.13—0.29）11.001.2712.27江蘇6.02 （4.88—7.12）1.58 （0.99—2.16）10.082.6612.74江西2.88 （2.39—3.36）1.05 （0.67—1.41）10.583.8314.41遼寧2.70 （1.88—3.51）0.52 （0.32—0.73）8.771.7010.47內蒙古1.50 （1.05—1.94）0.14 （0.08—0.19）11.051.0012.05寧夏0.36 （0.25—0.47）0.06 （0.03—0.08）8.721.3410.06青海0.42 （0.29—0.55）<0.0111.720.0211.74山東4.87 （3.34—6.41）1.84 （1.11—2.54）6.392.418.80山西1.93 （1.42—2.43）0.32 （0.19—0.44）9.471.5611.03陜西3.77 （3.07—4.45）0.28 （0.17—0.39）15.201.1316.33上海1.57 （1.30—1.83）0.38 （0.24—0.51）11.472.7714.24四川7.48 （6.22—8.71）1.00 （0.62—1.36）12.611.6814.29天津0.50 （0.34—0.65）0.21 （0.13—0.29）6.782.849.62西藏0.18 （0.12—0.23）<0.0110.920.1911.11新疆1.07 （0.75—1.38）0.19 （0.12—0.26）9.291.6610.95云南3.78 （3.12—4.42）0.09 （0.06—0.12）12.900.3013.20浙江4.02 （3.33—4.69）1.19 （0.75—1.60）11.253.3314.58全國84.24 （65.93—102.20）23.58 （14.69—32.17）8.362.3410.70北方31.69 （22.37—40.89）8.44 （5.11—11.67）8.042.1410.18南方52.55 （43.55—61.31）15.14 （9.57—20.50）10.503.0213.52

表  4   2020年全國31個省、自治區(qū)、直轄市不適溫度相關的健康經濟學損失 （均值及95%置信區(qū)間） 及其占GDP的比例

Table  4   Health economic loss （mean value and the 95% confidential intervals） and its proportion of local GDP due to non-optimal ambient temperature in 31 provinces，autonomous regions and municipalities of China in 2020

變量健康經濟損失（億元）GDP比重（%）低溫高溫低溫高溫匯總 安徽655.17 （525.16—782.63）186.93 （115.88—255.54）1.690.482.17北京365.21 （251.66—478.19）157.44 （95.66—217.01）1.010.441.45重慶485.37 （400.49—568.59）172.35 （109.95—231.46）1.940.692.63福建417.19 （341.79—492.17）248.69 （157.24—336.60）0.950.571.52甘肅218.53 （155.38—280.80）17.17 （10.34—23.94）2.420.192.61廣東722.47 （590.24—854.76）794.62 （502.46—1075.39）0.650.721.37廣西304.90 （250.36—358.92）219.78 （138.83—297.71）1.380.992.37貴州541.49 （450.21—629.79）30.78 （19.12—42.37）3.040.173.21海南21.78 （17.62—26.03）71.30 （45.10—96.46）0.391.291.68河北563.05 （388.44—736.48）186.75 （113.06—258.32）1.560.522.08河南646.19 （454.66—839.09）342.17 （208.45—471.01）1.170.621.79黑龍江412.26 （290.72—528.21）31.47 （18.91—43.86）3.010.233.24湖北893.03 （741.70—1039.37）190.96 （120.27—259.37）2.060.442.50湖南1042.28 （865.44—1213.75）301.63 （191.34—407.02）2.490.723.21吉林301.10 （211.25—387.59）34.77 （20.90—48.43）2.450.282.73江蘇1677.22 （1361.11—1985.60）441.62 （275.99—600.97）1.630.432.06江西518.71 （430.06—604.96）188.07 （119.71—252.96）2.020.732.75遼寧568.79 （395.32—738.74）110.05 （66.33—152.87）2.260.442.70內蒙古304.01 （213.12—391.68）27.59 （16.57—38.47）1.750.161.91寧夏59.10 （40.93—77.03）9.11 （5.48—12.68）1.510.231.74青海65.24 （45.16—85.06）0.08 （0.05—0.12）2.17<0.012.17山東1029.06 （705.85—1353.60）388.57 （235.33—537.30）1.410.531.94山西313.03 （230.34—394.24）51.46 （30.97—71.59）1.770.292.06陜西635.71 （517.19—749.19）47.07 （28.61—65.20）2.430.182.61上海728.41 （604.45—848.73）175.73 （111.39—237.37）1.880.452.33四川1275.01 （1059.05—1484.46）169.60 （105.97—232.23）2.620.352.97天津140.46 （96.72—184.06）58.74 （35.67—81.00）1.000.421.42西藏24.88 （17.18—32.54）0.44 （0.28—0.60）1.310.021.33新疆164.07 （114.68—212.00）29.34 （17.78—40.55）1.190.211.40云南565.03 （466.24—662.05）13.30 （8.26—18.30）2.300.052.35浙江1352.33 （1120.96—1577.42）399.78 （253.76—539.22）2.090.622.71全國17011.08 （13353.51—20597.72）5097.35 （3179.66—6945.93）1.670.502.17北方5965.68 （4199.65—7710.45）1685.66 （1021.42—2329.79）0.590.170.76南方11045.40 （9153.85—12887.27）3411.69 （2158.24—4616.14）1.090.341.42

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