Health and Climate Impacts of Scaling Adoption of Liquefied Petroleum Gas (LPG) for Clean Household Cooking in Cameroon: A Modeling Study.

Background: The Cameroon government has set a target that, by 2030, 58% of the population will be using Liquefied Petroleum Gas (LPG) as a cooking fuel, in comparison with less than 20% in 2014. The National LPG Master Plan (Master Plan) was developed for scaling up the LPG sector to achieve this target. Objectives: This study aimed to estimate the potential impacts of this planned LPG expansion (the Master Plan) on population health and climate change mitigation, assuming primary, sustained use of LPG for daily cooking. Methods: We applied existing and developed new mathematical models to calculate the health and climate impacts of expanding LPG primary adoption for household cooking in Cameroon over two periods: a) short-term (2017–2030): Comparing the Master Plan 58% target with a counterfactual LPG adoption of 32% in 2030, in line with current trends; and b) long-term (2031–2100, climate modeling only), assuming Cameroon will become a mature and saturated LPG market by 2100 (73% adoption, based on Latin American countries). We compared this with a counterfactual adoption of 41% by 2100, in line with current trends. Results: By 2030, successful implementation of the Master Plan was estimated to avert about 28,000 (minimum=22,000, maximum=35,000) deaths and 770,000 (minimum=580,000 maximum=1 million) disability-adjusted life years. For the same period, we estimated reductions in pollutant emissions of more than a third in comparison with the counterfactual, leading to a global cooling of −0.1 milli °C in 2030. For 2100, a cooling impact from the Master Plan leading to market saturation (73%) was estimated to be −0.70 milli °C in comparison with to the counterfactual, with a range of −0.64 to −0.93 milli °C based on different fractions of nonrenewable biomass. Discussion: Successful implementation of the Master Plan could have significant positive impacts on population health in Cameroon with no adverse impacts on climate. https://doi.org/10.1289/EHP4899

. Mid-year population size estimates that were used in health and climate modeling. Years 2011 to 2030 are from the National Statistics Institute of Cameroon (INS 2011, GLPGP 2016. For the subsequent years, after 2030 and up to 2100, we used the United Nations population projections (UN DESA 2017) and calibrated them to the National Institute of Statistics of Cameroon projections. Table S2. Modeled mean and standard deviation from the log-normal distributions with the best fit to the LPG Adoption in Cameroon Evaluation (LACE) exposure data for women (cooks) and children (<5). Table S3. Cameroon disease burden data from the Global Burden of Disease 2016 database for years 1990 to 2016. Available from: http://ghdx.healthdata.org/gbd-results-tool?params=gbd-api-2016-permalink/ba8cb190a7fdf0d999935fee909e5fa1. Numbers are rates per 100,000 population rounded to the first decimal digit. We used these data to fit the disease burden forecast models. DALY denotes disability-adjusted life years; YLL denotes years of life lost.  Table S5. Global warming potential (GWP) and global temperature change potential (GTP) values and the emission metric parameterization used (time horizons in years given in brackets). These values are unitless and indicate how strong emissions are relative to CO 2 . We have followed the parameterization used in IPCC (2014), the chapter of Myhre et al.(2013), with the exception of the upward revision of CH4 due to newer research (Etminan et al., 2016) finding stronger radiative forcing due to processes previously not accounted for. Here, we cite the original source.            61854003  10262772  12370801  2072  62624538  10390618  12524908  2073  63395074  10518465  12679015  2074  64165610  10646312  12833122  2075  64936146  10774159  12987229  2076  65680477  10897658  13136095  2077  66424809  11021157  13284962  2078  67169140  11144656  13433828  2079  67913472  11268154  13582694  2080  68657803  11391653  13731561  2081  69366180  11509187  13873236  2082  70074556  11626720  14014911  2083  70782933  11744253  14156587  2084  71491309  11861787  14298262  2085  72199686  11979320  14439937  2086  72861986  12089208  14572397  2087  73524287  12199097  14704857  2088  74186587  12308985  14837317  2089  74848888  12418874  14969778  2090  75511188  12528762  15102238  2091  76120269  12629820  15224054  2092  76729350  12730879  15345870  2093  77338431  12831937  15467686  2094  77947511  12932995  15589502  2095  78556592  13034053  15711318  2096  79108733  13125664  15821747 Year Mid-year total population size Mid-year population under 5 (assuming 16.6% of the total population size)

Households (assuming a mean household size of 5)
2097 79660873  13217275  15932175  2098  80213014  13308886  16042603  2099  80765155  13400497  16153031  2100  81317296  13492107  16263459 Notes: Mid-year population projection from 2015 to 2030, were only reported in 5-year intervals, and we used linear interpolation to estimate population sizes in-between the reported years.
We calibrated the UN projections (2031 onwards) by calculating the ratio between the two projections for the common years (2017-2030). We then projected the ratio assuming logarithmic growth and multiplied it with the median UN population projection. This produced a more conservative population projection than the median population projection from the UN, with better alignment to the official population projections from the National Statistics Institute of Cameroon.
For the number of children under the age of five, we used the estimate for the proportion of children under the age of five in 2005 which was approximately 16.6% of the population (INS 2011) and we assumed this proportion remained constant over time.
For the mean household size, we used the mean household size in Cameroon in 2011 which is five (INS and ICF International 2012) and we assumed that this also remained constant over time. LACE studies included 48-hr monitoring of 102 women, 56 children (< 5 years of age) from periurban and rural households in South-West Cameroon exclusively using wood fuel and 67 women and 60 children primarily using LPG fuel. (Pope et al. 2018a, Pope et al. 2018b We, used maximum likelihood estimation to fit a log-normal distribution to the LACE observed exposures by stove type.  Table S5. Global warming potential (GWP) and global temperature change potential (GTP) values and the emission metric parameterization used (time horizons in years given in brackets). These values are unitless and indicate how strong emissions are relative to CO 2 . We have followed the parameterization used in IPCC (2014), the chapter of Myhre et al.(2013), with the exception of the upward revision of CH4 due to newer research (Etminan et al., 2016) finding stronger radiative forcing due to processes previously not accounted for. Here, we cite the original source.