3. Economic and well-being benefits of better integrating gender equality and environmental goals

Abstract

Existing evidence shows that environmental factors have differentiated impact by gender, making women in some cases more impacted by certain environmental and occupational risks. Climate change and extreme weather events also affect men and women differently, with women often suffering most due to traditional gender roles. Developing quality infrastructure, which takes into account different needs by gender, and accelerating the transition to a low carbon economy could further women’s participation in the labour market. The effects of the current COVID-19 crisis should also be taken into account when developing policies to close gender gaps.

3.1. Key findings

Drawing on the OECD’s Well-being framework, this chapter reviews the existing evidence on the differentiated impact of environmental factors by gender and the benefits of better tailoring environmental policies to women’s and men’s needs and risk factors. While in OECD countries men suffer more premature deaths than women due to environmental and occupational risks, there are many non-fatal impacts that can reduce women’s well-being more significantly than men’s. Globally, more women than men die prematurely due to second-hand smoke, unsafe water sources, indoor air pollution, unsafe sanitation, and lack of access to handwashing facilities. Hazardous chemicals have also been found to have differentiated impacts on men and women.

Climate change also has a gender dimension. An increasing incidence and intensity of natural hazards such as droughts, landslides, floods and hurricanes tend to affect women more due to their greater economic vulnerability. In 2018 women accounted for more than 75% of displaced persons from such hazards (UNHCR, 2019[1]) Furthermore, traditional gender roles dictate that women become the primary caregivers for those affected by disasters – such as children, the injured, sick, and elderly – substantially increasing their emotional and material workload.

Addressing the specific environmental impacts on women and men can therefore save lives, reduce healthcare costs, improve well-being and reduce inequalities. In addition, incorporating a gender lens in policies that have an impact on the environment can generate numerous and broad economic benefits. There are three main areas of greatest relevance for OECD countries:

Ensuring a “just transition” to low carbon economies for men and women can increase productivity and lead to better economic outcomes and more resilient societies. Enhancing the participation of women in green innovation can be a source of high-skilled jobs for women and boost overall productivity;
Access to sustainable infrastructure (transport, energy, water, etc.) which meets women’s needs is a key requirement to enhance women’s economic empowerment and labour force participation. Designing such environmentally friendly infrastructure with a gender lens would provide win-win outcomes for all and improve well-being across the population.
Incorporating a gender lens into public policies such as product labelling, public information campaigns and targeted education programmes can help accelerate women’s contribution towards more sustainable consumption patterns and boost the overall sustainability of production and consumption.

The COVID crisis has also exposed many systemic weaknesses in societies, including women’s greater vulnerability to such crises. A “gendered” approach to the recovery can put economies on a greener and more sustainable path, by building better healthcare systems, increasing food security, developing more sustainable work and travel practices as well as more sustainable production and consumption patterns.

3.2. The environment’s impact on women’s health

The synergies between gender equality and environmental goals translate into positive economic and well-being outcomes across a number of dimensions. In particular, by advancing towards the nine environment-related SDGs, the benefits for women are observed in other SDGs such as SDG 1 (no poverty), SDG 3 (good health and well-being), SDG 4 (quality education), SDG 8 (decent work and economic growth) and SDG 10 (reduced inequalities). Similarly, improving gender equality and women’s economic empowerment can bring about both positive environmental impacts and improved economic prospects for all. This depends to a large extent on whether women are provided with the necessary education and awareness of environmental sustainability.

The OECD has developed a Well-being Framework that incorporates 11 dimensions of well-being. The environmental quality dimension addresses indicators such as exposure to outdoor air pollution and access to urban green spaces (OECD, 2020[2]). The framework has many parallels with the SDG indicators, but includes some dimensions not featured in the SDGs (e.g. relational aspects, or subjective well-being). It also has a more targeted number of indicators and methodically incorporates distributional measures (averages, inequalities across groups in the population – including gender – inequalities between top and bottom performers, and deprivations). Furthermore, the 11 dimensions of current well-being are complemented by four key resources for future well-being: economic, social, human and natural capital. They are measured in terms of stocks, flows, risk factors and resilience. Incorporating such a well-being framework in policy-making would go a long way to addressing and leveraging the gender-environment nexus.

Environmental and climate-related effects on human health – both physical and mental – exist all around the world. The environmental impacts on health outcomes depend not only on differences in exposure to environmental risks (e.g. arising from occupational exposures or differences in how domestic tasks like the cooking and cleaning are shared) but also on differences in vulnerability (e.g. baseline health, access to healthcare, knowledge of risks, biological differences etc.). These inequalities and inequities 1 by gender exist both in developing and developed countries, albeit with different intensities and natures. Overcoming them is a global challenge often requiring customised, local solutions. This is often the case in gender inequalities overall, even though the degree varies depending on the country, the income level, the geographical location etc. Even though the level of inequalities may be reducing in developed countries, when comparing with the rest of the world, this should not be interpreted as achievement of gender equality. Contrary, it may be an issue of inequalities existing under different issues (for example, women’s role in fetching fuel and water is mainly a developing economy issue, however, women facing more frequently energy poverty is a developed and emerging economies’ issue).

Throughout this chapter, data presented on mortality estimates are from the Global Burden of Disease project (GBD) (GBD, 2019[3]). GBD is a systematic, scientific effort to quantify the comparative magnitude of health loss due to diseases, injuries, and risk factors by age, sex, and geographic regions over time. Environmental and occupational risks accounted for 14% of premature deaths in 2019 in OECD countries, while the global average was at 36% (Figure 3.1.). Though the percentage is relatively small compared to other contributors to mortality in OECD countries, such as unhealthy lifestyles and the development of metabolic disease, environmental and occupational risks remain very important because they help interpret the linkages between human activity and environmental effects. They also provide background for estimating and mitigating exposure to harmful environmental and other agents that affect both the environment and public health.

Figure 3.1. Share of total premature deaths attributable to environmental and occupational risks in percentage

In OECD countries and globally, poor air quality --specifically ambient particulate matter (PM) -- constitutes the main contributor to premature deaths attributed to environmental and occupational risks, accounting respectively for 5.5% and 11.8% of total premature deaths , in 2019 (Roy and Braathen, 2017[5]) (Figure 3.2). For OECD countries, the other main environmental and occupational contributors to premature deaths are occupational carcinogens (2.9%) and second-hand smoke (2.3%). At a global level, the other main contributors are indoor air pollution (residential PM) (6.6%) and unsafe water sources (3.5%) (OECD, 2021[4])

As seen in Figure 3.2, 44% of premature deaths attributed to environmental and occupational risks are linked to air pollution in OECD countries. Globally, air pollution is linked to 54% of premature deaths. For OECD countries, about 26% of premature deaths derive from environment-related occupational risks, while globally this risk factor accounts for 7%. Such stark difference can be explained mainly by the share of deaths attributed to occupational carcinogens, which in OECD countries almost triples the world average. Occupation carcinogens include a series of agents to which the population is exposed through different economic activities (arsenic, benzene, beryllium, cadmium, chromium, diesel engine exhaust, formaldehyde, nickel, polycyclic aromatic hydrocarbons, silica, sulphuric acid, and trichloroethylene). These cause a wide range of cancers; cancers of the lung and other respiratory sites, followed by skin, account for the largest proportion (OECD, 2020[6]). The dominant routes of exposure are inhalation and dermal contact. Globally, almost 21% of premature deaths are attributed to unsafe water, sanitation and handwashing.

Figure 3.2. Risk factors contributing to environmental and occupation-related premature deaths by share of deaths attributed in percentage

Environmental and occupational risk factors have differentiated effects on men and women. Men seem to be more vulnerable than women, accounting in general for more premature deaths due to environmental and occupational risks in 2009 both in OECD and non-OECD countries (Figure 3.3). But some exceptions exist. For OECD countries, women show higher levels than men of premature death due to household air pollution from solid fuels. This is estimated based on the proportion of households using solid cooking fuels including coal, wood, charcoal, dung, and agricultural residues (OECD, 2020[6]). Globally, more women than men die prematurely due to second-hand smoke, unsafe water sources, unsafe sanitation, and no access to handwashing facilities according to the model developed with the data from GBD (Figure 3.3). It is worth noting that elderly women are disproportionately impacted by these risks. This could be attributed to differences in life expectancy.

Figure 3.3. Premature deaths attributed to environmental and certain occupational risks by gender per million inhabitants

Examining the trends over the past 30 years is encouraging. Deaths caused by environmental and occupational risks have been decreasing globally and in OECD countries. This drop in OECD countries, for both men and women, can be attributed mainly to improvements in ambient particulate matter (PM). Since 1990, an 18% decrease in environment-related premature deaths has been observed in OECD countries. However, not all risks in Figure 3.3 have been decreasing over that period (OECD, 2021[4]).

Despite the drop in premature deaths from environment-related risks, the welfare costs of these deaths remain considerable. The costs for all OECD countries amount to approximately 6.8% of GDP, equivalent to about USD 4 trillion for 2019 (Figure 3.4). Welfare costs are estimated to be less than 5% of GDP in only ten OECD countries (Iceland, Sweden, Ireland, Norway, Finland, New Zealand, Australia, Luxembourg, Canada and Israel), while Hungary’s welfare cost reaches almost 17%. Expressed per capita, this is equivalent to around USD 1 000 to 5 000 per capita per year among OECD countries. Globally, welfare costs surpassed 17% of global GDP in 2017, mainly due to excess costs for India and China.

Figure 3.4. Welfare cost of premature deaths attributed to environmental and occupational risks

3.2.1. Cost of air pollution

Concentration of and exposure to certain pollutants has increased over the last decades (Manisalidis et al., 2020[7]). With 91% of the world’s population living in places where air pollution exceeds WHO guideline limits, poor air quality poses the single biggest threat to human health, accounting for 3 to 4 million premature deaths per year (Roy and Braathen, 2017[5]), and shortening life expectancy by 1.8 years globally (Prüss-Üstün, Corvalán and WHO, 2006[8]). Furthermore, increasing global temperatures often exacerbate the effects of pollution on human health with urban areas being most affected (OECD, 2016[9]). Only 2% of the global urban population lives today under what is identified as acceptable PM₁₀ concentration levels (per WHO Air Quality Guidelines) (OECD, 2012[10]). Different studies show particularly negative correlations between high concentrations of air pollutants and health of humans, with women, the elderly and children displaying greater vulnerability (Balestra and Sultan, 2013[11]); (Inyinbor et al., 2018[12]).

Figure 3.5 shows the welfare cost of premature deaths from outdoor air pollution associated with PM_2.5 and ozone concentrations. The OECD average is 2.7% of its GDP while for BRIICS is triple the percentage. Welfare cost for men is higher than for women, as a percentage to the GDP, for all countries. Figure 3.6 on the other hand shows the development of averages in the World, OECD countries and BRIICS countries from 2008 to 2019; BRIICS countries have been on the rise, whereas the World and OECD averages plateau.

Figure 3.5. Welfare cost of premature deaths from outdoor pollution by gender

Figure 3.6. Welfare cost of premature deaths attributed to outdoor air pollution

Indoor air pollution can also pose a serious threat to human health, mainly affecting women and children in developing countries (WHO, 2016[13]), (Okello, Devereux and Semple, 2018[14]). According to GBD, in 2019 over 2 million people died prematurely in the world due to household air pollution from solid fuels and over 18 000 in OECD countries (GBD, 2019[3]). Although indoor air pollution constitutes for a greater threat to developing countries and emerging economies, it also remains important for OECD countries. Beyond the death toll, household pollution is linked to a non-negligible welfare cost for some OECD members (Figure 3.7). For OECD countries, the 2019 welfare cost of premature deaths attributed to indoor pollution was 0.075% of GDP equivalent. While this might be a small percentage, a closer look to the data shows stark differences between OECD countries, ranging from 0.001% welfare cost in Switzerland to 2.8% in Hungary. It is also worth noting that the welfare cost from indoor air pollution is higher for women, whereas the welfare cost from outdoor air pollution is higher for men, in both OECD and non-OECD countries. This result is consistent with the findings that men spend more time outdoors, such as for traveling to work, and that women spend more time cooking and heating the house (WHO, 2016[15]).

Figure 3.7. Welfare cost of premature deaths attributable to indoor air pollution

SDG 3 on ensuring healthy lives and promoting well-being for all and at all ages, includes a specific target focusing on substantially reducing the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination. All three indicators identified under the gender-environment nexus fall under the specific SDG 3 target (Table 2.2). Data availability allows for measuring mortality rates attributed to household and ambient air pollution (SDG indicator 3.9.1).

Increased air pollution exposure causes a variety of health problems, including decreased lung function, aggravated asthma, chronic bronchitis, diabetes, irregular heartbeat, nonfatal heart attacks, and contribute to premature death in people with heart and lung disease (OECD, 2012[16]); (OECD, 2014[17]). Research links increased levels of PM in the air with respiratory and cardiovascular diseases, noting that the effects are more negative for children and the elderly compared to adults (Aragón, Miranda and Oliva, 2017[18]).

Ambient air pollution influences levels of infant mortality and morbidity, especially in the first weeks of a child’s life, while there are also indications linking pregnant women’s exposure to air pollutants with negative effects on the foetus (Bové et al., 2019[19]); (Currie and Neidell, 2004[20]). Increased levels of NO₂, produced usually by fuel combustion in diesel vehicles, by 10-ppb per week, have been associated with a 16% rise in the probability of pregnancy loss. This can have the same harmful effect on the foetus as tobacco smoking during the first trimester of pregnancy (Carrington, 2019[21]); (Saha et al., 2007[22]).

Low-income households located close to freeway traffic are among the most affected, with children’s health suffering most; (Suissa and Edwardes, 1997[23]) (Gauderman et al., 2007[24]). The 2019 OECD study “The Economic Cost of Air Pollution: Evidence From Europe” shows that exposure to air pollution correlates with education and income levels in European member countries (Dechezleprêtre, Rivers and Stadler, 2019[25]). As a result, air pollution can exacerbate socio-economic gaps and further contribute to the intergenerational transmission of poverty. Compared to rural dwellers, urban dwellers in OECD countries are less satisfied with their local environmental quality. (Balestra and Sultan, 2013[11]). As women spend more time walking than men (who spend more time driving individual cars), they are exposed to different sources of urban air pollution that could potentially lead to differentiated effects on their health (ITF, 2018[26]). Even in cases where both men and women use private cars for work, there may be differences in levels of exposure, depending on location, geography and different daily patterns between men and women (Setton et al., 2010[27]). Research shows that because of biological factors, women are more vulnerable to environmental pollution (Butter, 2006[28]). In developing countries, charcoal production by main roads is also a major source of pollutants affecting the health of those who spend long periods walking along them (Girard, 2002[29]).

The aforementioned OECD study on the economic cost of air pollution in Europe finds also a correlation between increased air pollution and productivity levels and economic activity (Dechezleprêtre, Rivers and Stadler, 2019[25]). The results show that increasing average annual concentration of PM_2.5 by 1μg/m³ reduces total GDP by 0.83% and decreases output by 0.80% per worker. These reductions could result from changes in work productivity (reduced work attendance and absenteeism) as well as the direct effect of pollution on some sectors. When focusing particularly in the agricultural sector, evidence in Europe shows that a 1 μg/m³ increase in PM_2.5 concentration in air can reduce agricultural gross value added by 4.6%, both due to the environmental effects and shifts in workers’ productivity (Dechezleprêtre, Rivers and Stadler, 2019[25]). This economic analysis supports existing literature on the detrimental effect of air pollution to human health and agricultural yields (Agrawal et al., 2003[30]); (Chay and Greenstone, 2003[31]). A gender dimension is particularly relevant in countries where women represent more than half the rural population, e.g. Central and Eastern Europe (non-EU) (Kovačićek and R. Franić, 2019[32]), or in areas where women are in charge of sustenance agriculture.

Numerous other studies confirm the negative social and economic impact of air pollution. A survey in Lima, Peru, shows that households with dependent members (i.e. children, elderly) are more severely affected during days with higher levels of air pollution, than those without. A 10 μg/m³ increase in PM_2.5 levels leads to a reduction of two working hours per week per household, as care responsibilities increase (Aragón, Miranda and Oliva, 2017[18]). This could imply that women, responsible for the caregiving tasks in the household, are the ones most affected by high pollution days. In Santiago, Chile, where extremely high pollution days – of over 100 μg/m³ of PM₁₀ – are common, women are more likely to stay at home with their children or elderly family members (Montt, 2018[33]). This doubles the gender gap in working hours between men and women, as women tend to reduce their hours worked in weeks of high pollution and men compensate by increasing theirs (Montt, 2018[33]).

In the context of COVID-19, growing evidence finds a clear link between exposure to air pollution and increase in susceptibility to viral infection (Abdo et al., 2011[34]). A recent study by the University of Harvard shows that an increase of 1μg/m3 in PM_2.5 is associated with an 8% increase in the COVID-19 death rate in the United States, adding to existing knowledge on increased risk for patients with cardiovascular and lung disease (Wu et al., 2020[35]). In supporting this statement, emerging evidence suggests that PM pollution has increased the transmission rate of COVID-19 in Italian towns and cities (Setti et al., 2020[36]). Additional work identifies PM itself as a vector for the transmission of viruses and a cause for the increased vulnerability to diseases due to air pollution exposure (Setti et al., 2020[37]). Furthermore, in previous coronavirus outbreaks in China, such as the one in 2002, analysis showed that patients from regions with high air pollution levels had twice the mortality risk of patients from regions with low pollution levels (Cui et al., 2003[38]).

Although men appear to be more likely to die from the current virus, this pandemic illustrates that its impact is not limited to biological determinants but is influenced by social norms as well (Zhonghua, Xing and Z, 2020[39]). These in turn differentially affect health behaviour of women and men. For instance, the European Institute for Gender Equality calls for clinical trials for a COVID-19 vaccine to include a gender-balanced representation of women, to prevent a differentiated gendered effect (EIGE, 2020[40]). Minimising individual vulnerability to infection should be a top priority for countries, as should consideration of the gender dimension and its differentiated impact.

Analysis also highlights that there is an increasing link on the economic consequences of air pollution and climate change, with the negative effects visible in many Asian and African economies. Outdoor air pollution is an emerging problem in Africa, driven by increased traffic, power generation and industries. Roy (2016) estimates that dirty air could be killing 712,000 people a year prematurely, compared to approximately 542,000 deaths from unsafe water and 391,000 from unsafe sanitation (Roy, 2016[41]).

Other emerging evidence attempts to provide a link between air pollution and mental and physical health, cognitive performance, and even violent behaviour. Kioumourtzoglou et al. (2017) show that long-term exposure to elevated levels of PM_2.5 and ozone in the United States increases the risk of depression in middle-aged and older women (Kioumourtzoglou et al., 2017[42]), while others show links between depression and air pollution (Xin, Xiaobo and Xi, 2015[43]). On the other hand, contemporaneous and cumulative exposure to air pollution appears to affect men’s cognitive performance more negatively than women’s (Chen, Zhang and Zhang, 2017[44]).

Recent studies have also linked increased exposure to PM_2.5 and ozone to aggressive behaviour and increased domestic violence (Nickerson, 2019[45]); (Burkhardt et al., 2019[46]). Experimental behavioural analysis of people living in the United States and Indian cities showed that air pollution elevates anxiety, triggering unethical behaviour among adults (Lu et al., 2018[47]). Further recent findings in the United States by Burkhardt et al (2019) suggest a link between air pollution and violent behaviour (Burkhardt et al., 2019[46]). More specifically, they calculate that – for the period between 2006 and 2013 – a 10% increase in PM_2.5 and a 10% increase in ozone were associated with 0.14% and 0.3% increases in violent crimes and assault, respectively (Burkhardt et al., 2019[46]). These correlations are valid both for assaults in and out of the house, linking especially changes in outdoor PM_2.5 with domestic violence.

Women were statistically the main victims of domestic violence between 2003 and 2012 in the United States (76% female victims vs. 24% male) (Truman and Morgan, 2014[48]). Burkhardt et al. (2019) also calculate the financial benefits from decreased violence as a result of a reduction in air pollution (Burkhardt et al., 2019[46]). According to their calculations, a 10% reduction of PM_2.5 concentrations could reduce crime costs by more than USD 400 million, and a 10% reduction of ozone concentrations by USD 1 billion per year. Similar findings in London link increased crime activity to ambient air pollution (Bondy, Roth and Sager, 2018[49]).

3.2.2. Water and soil contamination

Water contamination is a growing problem, affecting women in particular. In the context of COVID-19, water access points can become clusters of infection, which mainly affect women. Therefore, ensuring safe access to clean water is key to mitigating infection. Up to 80% of illnesses in the developing world are linked to inadequate water quality and poor sanitation (Fauconnier, Jenniskens and Perry, 2018[50]). Every year, unsafe water sickens about 1 billion people. Water pollution caused 1.8 million deaths in 2015, according to The Lancet (Landrigan et al., 2018[51]). It is estimated that over 800 000 people die each year from diarrhoea as a result of unsafe drinking water and poor sanitation and hand hygiene (WHO, 2014[52]). In low-income countries, women are more exposed to the transmission of diseases because they are often in charge of disposing of dirty water and human waste and they rarely have access to safe or private sanitation facilities (WHO and UNICEF, 2017[53]). Even in developed countries, polluted water is a major concern influencing women’s health foremost (Landrigan et al., 2018[51]) (Watts et al., 2019[54]) (Woodcock et al., 2009[55]).

SDG 3 has one indicator on the health impact of water contamination, which also falls under the gender-environment nexus. This underscores the importance of safe water for women and the environment. Indicator 3.9.2 on mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene provides further insight on how women are more influenced than men from exposure to unsafe water, and from a lack of sanitation and handwashing facilities. Whereas in OECD countries, the number of premature deaths from unsafe water, sanitation and lack of hygiene is limited, and the welfare cost is minimum barely reaching 1% in GDP equivalent, this is not the case at the global level (Figure 3.8). Globally, women are clearly more affected, and female premature deaths’ welfare cost as percentage to GDP at 2% is higher than for men. The main contributor to these deaths is the lack of access to safe water.

Figure 3.8. Welfare cost of premature deaths related to unsafe water, sanitation and handwashing

Despite limited premature deaths due to unsafe water, sanitation and hygiene (WASH) in OECD countries, water and soil contamination remains a concern. A recent OECD study indicates that there are increasing environmental concerns from active pharmaceutical residues in freshwater (OECD, 2019[56]). As the consumption of such products increases, better monitoring and assessment of the effects of such ingredients on the environment is needed, as are improvements to the treatment of water resources. The study also points out the need to more thoroughly examine the effect of such pharmaceutical ingredients and mixtures on human health, especially the most sensitive groups of the population, such as pregnant women, foetuses and children (OECD, 2019[56]).

Water and soil contamination have a greater impact on women from minority groups and lower income levels. This is because they face greater difficulties avoiding pollution by moving to cleaner locations, for instance. In a study on the state of New Jersey, United States, Currie et al. (2013) find a correlation between women’s level of education and the probability of a household moving as a result of contaminated water. This indicates a clear effort by women to protect themselves and their families from environmental harm (Currie et al., 2013[57]).

There are many examples of excessive use of toxic chemicals (e.g. pesticides) in agriculture, where women in many developing countries represent about 70% of the labour force. In Tanzania, for example, women do the planting and harvesting, and even the mining, whereas men do the more “mainstream’’ dangerous jobs (Mrema et al., 2017[58]); (Roser and Ritchie, 2020[59]); (Lal, 2020[60]). The United Nations Development Programme (UNDP) has been working extensively on identifying guidelines for the sound management of chemicals in developing countries. As part of this work, it has been strengthening the gender dimensions (UNDP, 2011[61]).

The extensive use of hazardous chemicals can potentially affect women more than men, especially in rural areas of developing countries where women are highly dependent on natural resources (UNEP, 2013[62]). The impacts of plastic litter, air pollution, mercury and other pollutants on animal and plant biodiversity have been widely documented (Lovett et al., 2009[63]); (IPBES, 2019[64]) and tend to have a greater impact on traditional and indigenous populations, with a specific incidence on women [see (Inyinbor et al., 2018[12]) for the effects of heavy metal pollution on pregnant women].

A recent study on a number of European countries has provided evidence of the continuing problem of toxic chemicals and metals in fish consumed by pregnant women and children. It also compares the concentrations of hazardous compounds contained in organic and conventionally grown produce due to pesticides (Papadopoulou et al., 2019[65]).

Water and soil contamination bear an economic cost to society that further supports the business case for abatement efforts. It is clear that mitigation costs bear a much higher price than adaptation costs in the case of soil and water contamination, as purifying these resources could pose great challenges and economic burdens. The cost of unsafe water sources for OECD countries is calculated by the welfare cost of premature deaths, which represented 0.03% of member states’ GDP in 2019 (GBD, 2019[3]) (Roy and Braathen, 2017[5]). Women play a crucial role in both adaptation and mitigation stages and should therefore be considered when designing policy recommendations. They are key players in implementing and securing a path towards sustainable development in the context of water and soil management.

3.2.3. Other exposure to hazardous chemicals

The burden of disease from exposure to hazardous chemicals is significant worldwide and falls more heavily in non-OECD countries where good chemical safety measures are not always in place (OECD, 2018[66]). Men and women are exposed to chemicals on a daily basis, both at home and at work. The level of exposure, however, may differ depending on the length of exposure and be exacerbated by additional stressors such as heat waves [see the example of Paris (Lemonsu et al., 2015[67]) and (McGregor, 2015[68]). In addition, there are gender-differentiated impacts based on women and men’s physiological, hormonal, and enzyme differences, potentially posing differentiated risks related to absorption, distribution, metabolism, storage and excretion.

Chemical substances such as Persistent Organic Pollutants (POPs), heavy metals and Endocrine Disrupting Chemicals (EDCs), have been widely identified as having differentiated impacts on men and women (Street et al., 2018[69]); (WHO, 2016[70]). In a recent study on pregnant women, EDC mixtures were found to have adverse health effects on new-born babies’ and children’s neurodevelopment, metabolism and growth, among others, and hence affecting mental and physical health of their mothers (Bergman, Rüegg and Drakvik, 2019[71]).

As the non-OECD countries’ share of the world’s chemical production increases, the burden of diseases attributed to hazardous chemicals exposures is expected to grow. According to OECD calculations, a six-fold increase in chemical production in non-OECD countries is expected by 2050, mainly in the major emerging economies, such as Brazil, Russian Federation, India, Indonesia, China and South Africa (BRIICS) (OECD, 2012[10]). This would also increase the risk of exposure, especially for the most vulnerable populations.

Figure 3.9. SDG indicator 3.9.3 - Mortality rate attributed to unintentional poisonings, by sex

Within the SDG Framework, the indicator for measuring the decreased number of deaths and illnesses from hazardous chemicals only measures the mortality rate from unintentional poisoning (SDG indicator 3.9.3). According to data available for OECD countries, men are more often the victims of unintentional poisoning than women in most countries (Figure 3.9). However, further data disaggregation would be necessary to detect the differentiated impacts of poison sources by gender. Although in OECD countries on average, men’s deaths attributed to unintentional poisoning were almost double that of women, the global scale on average shows a marginal difference between the two sexes (Figure 3.10).

Figure 3.10. Mortality rate attributed to unintentional poisonings, by gender

Social factors determine differences in men and women’s exposure to hazardous chemicals based on traditional labour segregation and different consumption patterns. Despite the fact that women are participating increasingly in the labour market, occupational exposure to certain carcinogenic agents is still monitored mainly on males (Hohenadel et al., 2015[73]). This is confirmed by the available data for OECD and non-OECD countries, where what is mainly measured is the number of premature deaths caused by occupational carcinogens or occupational particulate matter, gases and fumes (Figure 3.11). This can be interpreted as job segregation, whereby production and use of chemicals is more characteristic of male-dominated sectors. The welfare costs of premature deaths by occupational risks as percentage of GDP equivalent are also in line with the number of deaths. It is interesting to note that the most substantial welfare costs are due to occupational carcinogens in OECD countries, and due to occupational PM, gases and fumes globally; the latter is applicable to both men and women (Figure 3.11).

Figure 3.11. Welfare cost of premature deaths attributed to environmental and occupational risks by gender

However, studies on occupational exposure identify differences in exposure levels to various chemicals between male and female workers. A study in Italy has identified higher levels of exposure to certain chemicals for men or women related to task segregation in the wood industry and in furniture manufacturing (Scarselli et al., 2018[74]). It is clearly necessary to take into account women’s and men’s differing occupational roles and exposure in order to conduct meaningful research and monitor the effects of chemicals on human health.

Research also shows differences in chemical exposure even when men and women have the same occupation (based on job titles). The aforementioned Italian study identified higher levels of exposure to nickel and chromium VI compounds for women working as machine operators, when compared to the predominately-male workforce in the sector (Scarselli et al., 2018[74]). Other research, trying to identify the non-biological reasons for these differences, has linked them to differences in cognitive skills and how men and women perform certain tasks differently (Czaja et al., 2006[75]); (Arbuckle, 2006[76]). Moreover, the work environment in some traditionally male-dominated sectors is usually adapted to men’s needs, so protective uniforms or gloves may not be adequately sized for women (Arbuckle, 2006[76]). There is room for further examination on the ergonomic nature of jobs (i.e. handling heavy machinery, or repetitive aerobic movement), as some could make women to exert more effort, leading – beyond other health impacts – to an increased breathing rate and thus a higher intake of chemicals.

Another example of segregated labour is the textile and footwear industries. Since the mid-2000s, production is concentrated in Asian countries, which now account for 62% of global exports, and which are expected to become the major consumers of clothing by 2025 (ILO, 2019[77]). The majority of workers in the textile and footwear industries and supply chain are women (80%). This sector’s workers face unsafe working conditions such as exposure to chemical substances (colouring, dyes, adhesives and primers, lack of protective materials and lack of sanitation and hygiene facilities), among other factors (Ahmed et al., 2004[78]). The OECD’s Due Diligence Guidance for Responsible Supply Chain in the Garment and Footwear Sector provides a list of recommendations and a toolkit that helps companies assess their environmental and social performance, and to integrate gender equality, health and environmental issues into their due diligence (OECD, 2018[79]).

Due to social norms, socio-economic status and demographic trends, women are often in charge of household management. They thus tend to be more in contact with household cleaning products and waste (such as faeces), which increases their exposure to certain hazardous chemicals and toxic substances (Hertz-Picciotto et al., 2010[80]). Women are also more exposed to chemicals in personal care products, such as cosmetics and even jewellery (UNDP, 2011[61]). Recent US data, for example, showed that women, as the major consumers of personal care products, are more exposed than men to mercury, parabens and phthalates (all ingredients in beauty products) (Zota and Shamasunder, 2017[81]).

OECD work has supported governments in their efforts to assess the risks of human exposure to individual chemicals 2. More specifically, the OECD Guidelines for the Testing of Chemicals provide internationally accepted standard methods to assess the potential effects of chemicals (industrial, pesticides, personal care products, etc.) on humans and the environment (OECD, 2013[82]). Many of these tests evaluate sex-specific effects, which is particularly relevant for the evaluation of chemicals that disrupt the endocrine system. More research on the combined exposure to mixtures of chemicals and potential male- / female-specific effects is necessary, as chemicals are most often not found in isolation.

While the OECD has also been working on identifying the environmental impacts of plastics and plastic waste, more work could be carried out on their human health impacts. In a recent research paper, Ten Brink et al. refer to the potential hazardous effect on human health of various chemicals added to plastics (Ten Brink et al., 2016[83]). They refer especially to the potentially problematic use of plastic packaging for food and children’s toys; plastic sewage and water pipes and how chemical additives limit the recycling of plastic (OECD, 2018[84]). Such analysis should have a gender aspect, since women are most likely to be in contact with such products (e.g. plastic packaging for food) and are the decision-makers about waste management in the household (Lynn, Mantingh and Rech, 2017[85]).

The OECD is currently carrying out a project on the willingness-to-pay (WTP) to avoid chemicals-related negative health impacts (OECD, 2018[86]). In a first phase, surveys will be implemented in selected countries to estimate the WTP to avoid asthma, IQ loss in children, low birthweight, kidney failure and fertility loss (Alberini et al., 2010[87]). As in most stated-preference surveys, gender is one of the socio-economic variables respondents are asked to answer. This could serve as an example for future studies (Cascajo, Garcia-Martinez and Monzon, 2017[88]).

3.2.4. Climate change

Women’s health is also affected differently by climate change and increased temperatures, both in OECD and non-OECD countries. For example, the 2003 heat wave in France led to the premature death of 15 000 people; the mortality rate for women was 75% higher than for men (Fouillet et al., 2006[89]).3 A 2019 study focusing on Spain showed that women of all ages are more susceptible to die of cardiovascular disease than men. Cardiovascular disease may be caused by exposure to high temperatures (Achebak, Devolder and Ballester, 2019[90]); (Yin et al., 2019[91]). Considering Spain has a mortality rate of 2,683 deaths per year from airbound pollution, and temperatures are rising, effects on women could be disproportionate (Ortiz et al., 2017[92]).

Climate change brings about a higher incidence and intensity of natural hazards such as droughts, landslides, floods and hurricanes. These hazards have a greater impact on more vulnerable populations because of their greater dependence on natural resources for their livelihoods, a lower capacity to adapt, lower quality dwellings and more exposed locations. Women, in particular, are disproportionately likely to lose their livelihoods, especially in developing regions, from the increased occurrence of hazardous events (UNEP, 2011[93]). As they account for the majority of the world’s poor, women often face higher risk and greater burdens from the impacts of climate change such as uncertainty of sustenance, health risks, etc. Extreme events such as droughts coupled with gender inequities lead to women having to bear disaster effects disproportionally (UN Women, 2018[94]).

Women also appear to be less able to adapt to climate change, as such adaptation is influenced by social and economic status as well as access to resources. An example of work leading global adaptation efforts is the G20 Climate Sustainability Working Group’s Adaptation Programme. It strives to ensure the inclusion of women in adaptation planning. Especially in non-OECD countries, women are essential in developing adaptation mechanisms due to their key role managing resources to sustain their households (UNEP, 2011[93]).

Lack of equal access to formal education, gender-based discrimination and social exclusion reduce women’s ability to cope effectively with the demands of climate change adaptation. In addition, climate change forces households to migrate, worsening both the gender gap and mitigation efforts (Fauconnier, Jenniskens and Perry, 2018[50]). A 2016 study of Nepalese households with members that have migrated shows that women and girls in the families decrease their weekly hours in less productive activities by 7.8% and 4.1% respectively, and increase more than proportionally the time they spend on productive activities (8.2% and 5.5% respectively), when compared to men and boys. It also showed that women tend to shift from wage-employment to sustenance farming and work in family farms (Phadera, 2016[95]).

The effects of climate change could lead, in the long and short-term, not only to unbearable economic costs but to increased gender disparity. Tackling these issues concurrently, by aligning SDGs, could result in a more effective, inclusive economic solution to climate change and other environmental issues.

3.3. Access to sustainable and quality infrastructure and economic opportunities for women

A particularly important economic channel of the gender-environment nexus is how better access to sustainable infrastructure for women (water, energy, transport, housing and social infrastructure, communications, etc.)4 can boost their labour market participation and productivity, while reducing environmental externalities. While in developing countries a gender gap exists across all types of infrastructure, in OECD countries the main concern is the inadequacies of transport and social infrastructure.5

To improve women’s access to and use of transport and social infrastructure, a number of factors must be taken into account. First, women’s specific travel patterns: they tend to be more irregular and varied than men’s, as women more often combine household, family and work duties. Studies have found a stronger negative correlation between commuting time and participation in the labour force for women than for men and women’s higher preference for flexible modes of transport as well as for public transport. EIGE’s Gender Equality Index shows that 24.5% of women use public transport, compared with 18% of men; 25% of them cycle or walk, compared to 20.25% of men. On the contrary, 57.5% of men use their car as the preferred mode of transport, compared to 48.75% of women. Eighteen per cent of single parents rely exclusively on public transport (EIGE, 2020[96]). Secondly, women’s greater exposure to harassment and physical violence reduces the attractiveness of public transport for them and their ability to work in certain neighbourhoods (ITF, 2018[97]). Cases show that women all around the globe restrict their use of public transport because they fear harassment or other forms of violence, sometimes due to past experiences (see more on this topic under Part II of this report, forthcoming). Measuring accessibility provided by sustainable transport, and adapting policy measures to the findings and needs, could help better serve women and men, while minimising environmental impact (OECD, 2019[98]).

The COVID-19 pandemic has upended travel patterns throughout the world, and affected public transport most. Even as mobility restrictions have been lifted in many countries, the attractiveness of public transport has declined compared to the pre-crisis situation because of the chances of contagion from close physical contact. Given their greater preference for public transport, women’s mobility has been particularly affected by the pandemic and its aftermath (EIGE, 2020[96]). People have been opting for alternative travelling modes, such as walking and cycling, especially since in many cases there were limitations placed on travel distances (ITF, 2020[99]). Women’s mobility patterns were, in a way, generalised during the COVID-19 crisis, and magnified on top by the physical distances rules, bringing to the forefront the need to adapt urban infrastructure to more gender-responsive requirements (ITF, 2020[99]). Mainstreaming gender could thus eventually lead to increasing urban resilience to shocks such as COVID-19.

In rural areas, where women’s livelihoods would improve drastically by sustainable infrastructure development (food, health, energy, water and sanitation, transport); the COVID-19 crisis seem to disproportionately affect women and girls the most. In these difficult times, rural women – both in developed and developing countries - seem to experience more challenges, due to unpaid care work, their employment informality, and their dependence on natural resources (Salcedo-La Viña, Singh and Elwell, 2020[100]) (EmPower, 2020[101]), as well as more gender-based violence in the household (Moffitt et al., 2020[102]). Sustainable transport infrastructure would provide easier access to women for their daily activities, and also a safer environment outside the house.

In parallel, the COVID-19 crisis has boosted remote working, shopping, financing and other activities, bringing to the forefront the need for resilient digital infrastructure. The existing gender digital divide, both in OECD and non-OECD countries, whereby women face more digital exclusion, needs to be overcome to guarantee women are not left behind (OECD, 2018[103]), especially considering women’s vulnerability to health crises from an employment perspective (OECD, 2020[104]). Analysis from the United Kingdom and the United States indicates that women were more likely to lose their jobs during the COVID-19 crisis, spend more time at home and take on more caring duties than usual (Adams-Prassl et al., 2020[105]). At the same time, the COVID-19 crisis may offer greater flexibility to digitally-savvy women to better combine work and home responsibilities, if a change in social and cultural norms leads more men to participate in the unpaid care work (Alon et al., 2020[106]). Irrespective of COVID-19, OECD analysis showed that digital technologies and improving access to digital infrastructure can increase women’s labour market participation and women’s economic empowerment (OECD, 2018[103]).

There are few studies on the economic benefits of improving women’s access to infrastructure. Initial analysis focuses on women’s role in unpaid care and household work, and how improving (sustainable) infrastructure can benefit women as end-users (Clancy, Skutsch and Batchelor, 2003[107]). Agénor and Agénor (2014) produced a framework applicable to low income countries, based on which access to infrastructure services improves women’s time allocated to market production and household activities, providing women with an income, improving children’s health and education and eventually contributing to economic growth (Agénor and Agénor, 2014[108]). Other analysis presents cases where better road, electricity and digital infrastructure led to an increase in women’s labour participation (Kabeer, 2012[109]). OECD estimates show that improvements in access to social infrastructure could increase (primarily) women labour market participation by around 3%, which would add 2.5% to the GDP per capita globally (Figure 3.12).

Figure 3.12. Impact of improved access to social infrastructure on GDP per capita

3.4. Women in green jobs and green innovation in the post-COVID 19 low-carbon transition

Increasing women’s participation in the labour force is both a gender equality and an economic imperative. OECD estimates made before the COVID-19 crisis suggest that, on average across OECD countries, halving the gender gap in labour force participation rates by 2040 could boost annual average GDP per capita growth rates by 0.04 percentage points, relative to the baseline. Going further and eliminating the gender participation gap could boost average annual GDP per capita growth by roughly 0.15 percentage points (OECD, 2018[110]). Ostry et al. (2018) argue that narrowing gender gaps in labour participation will bring even larger than expected economic gains, due to the production growth brought by gender diversity, and the welfare gains from removing social and other barriers. More specifically, they show that men and women complement each other at work, especially when women are scarce in a sector, leading to increased productivity and economic growth. Gender considerations can thus influence the benefits from labour re-allocation to sectors were women are not present (Ostry et al., 2018[111]). They finally indicate the need to overcome barriers for women’s labour participation, in line with what has already been presented by the OECD on supporting women’s economic empowerment through putting in place the necessary conditions (from legal rights to assets, participation in relevant education and training, to tackling informal barriers to their progress and discrimination in the market place) (OECD, 2012[112]).

With the climate and environmental crises luring, the urgency for a transition to a low carbon economy has grown exponentially. Such transition is expected to bring about major transformations in whole economic sectors. The COVID-19 crisis may also lead to an acceleration of some of these transformations, driven by the expansion in telecommunications and a preference for local production. The low carbon transition can also help reduce existing social and economic inequalities, including gender gaps, if it guarantees fairness and enhanced social cohesion (OECD, 2020[113])

The transition to a green economy and the introduction of green growth policies are foreseen to only bring marginal aggregate effects on labour (Chateau, Bibas and Lanzi, 2018[114]). According to (Chateau, Bibas and Lanzi, 2018[114]), the labour implications of climate and energy policies in OECD members are expected to be higher in sectors that rely mostly on labour, such as mining and quarrying, electricity, chemicals and food products. They conclude that the sectors where most low-skilled jobs will be lost are in mining and quarrying and electricity (especially fossil-fuel dependent). Conversely, jobs are expected to be created in transportation services and construction sectors.

Six main economic activities are the source of most GHG emissions, pollution and other forms of environmental damage: energy generation, mineral and metal extraction, manufacturing processes, agriculture, transport, and construction. With the exception of agriculture and some manufacturing processes (e.g. textiles), women tend to be most underrepresented in these sectors globally. In order to meet jointly economic, social and environmental goals, policymakers should therefore aim to increase women’s labour force participation in the greener versions of these economic activities such as renewable energy, sustainable agriculture, public transport, and cleaner manufacturing processes.

For instance, the FAO has estimated that equal access to land and other productive resources for women and men could increase total agricultural output in developing countries by 2.5% to 4% (FAO, 2011[115]). But it is equally important to consider that better access to land, credit, and technology for women could also improve the sustainability of agricultural practices, considering that women – especially in developing countries - are mostly small-scale holders that often follow traditional knowledge practices in their agricultural methods (see more under Chapter 6).

In OECD countries, women are not particularly present in the GHG emissions and energy intensive sectors (Figure 3.13). While on average around half of women in OECD countries were employed in 2018 (compared to over 65% of men), they are overwhelmingly concentrated in the services sector. The manufacturing, mining, energy, transport and construction sectors tend to be male-dominated. Based on ILO 2017 data for OECD countries, women occupy on average less than 10% of jobs in construction; just over 14% of mining and quarrying (including extraction of crude petroleum and natural gas); and almost 19% of manufacturing of coke and refined petroleum products. In transport, women occupy almost 22% of jobs. They are better represented in air transport (47%), and postal and courier activities (35%), but occupy only 22% and 12% of positions in water and land transport, respectively. Women also account for only 28% of the agricultural labour force (crop, animal production, hunting), about 20% of fishing and aquaculture labour force, and 17% of forestry jobs in OECD countries. These figures can be contrasted with the health and social sector, where around 70% of the workforce is female. Women even more heavily dominate the long-term care sector, holding, on average, about 90% of jobs (OECD, 2020[116]).

Figure 3.13. Labour by gender in specific economic activities in OECD countries

There are also important differences in female participation across manufacturing sectors. Women are mainly occupied in sectors linked to the manufacturing of household and personal use products, or to service provision. Examples are textile and garment manufacturing, the chemical product manufacturing (such as fertilisers, plastics and cleaning products) and with agricultural product manufacturing. In OECD countries, women occupy 55% of jobs in and around fashion manufacturing, and 32% of jobs in chemicals manufacturing.

The transition to a green economy and technological advancements are expected to shift jobs within these sectors and establish new, greener sectors of growth. The ILO provides the following definition of green jobs: “Green jobs are decent jobs that contribute to preserve or restore the environment, be they in traditional sectors such as manufacturing and construction, or in new, emerging green sectors such as renewable energy and energy efficiency. Green jobs help improve energy and raw materials efficiency, limit greenhouse gas emissions; minimize waste and pollution, protect and restore ecosystems, and support adaptation to the effects of climate change” (ILO, 2016[117])

Empirical evidence shows that women have a greater presence in the greener parts of these economic sectors, for instance renewable energy. A global survey conducted in 2018 by the International Renewable Energy Agency (IRENA) shows that women account for 32% of the workforce in the renewable energy sector, compared to 22% in the oil and gas industry sector. Yet most occupy administrative or non-STEM (Science, Technology, Engineering and Mathematics) technical positions (IRENA, 2019[118]).

Furthermore, a recent OECD report on the labour implications of the transition to a resource efficient and circular economy, calculates that green jobs, such as those in secondary-based metal production and recycling sectors are expected to increase by 27% and 48% respectively by 2040 (Chateau and Mavroeidi, 2020[119]). These are partly due to labour shifts from other sectors such as chemicals or textiles’ manufacturing. Moreover, these green jobs are expected to require medium and high skills.

Considering the benefits from women’s labour participation to economic growth, guaranteeing women’s engagement in green jobs could potentially be beneficial for the transition to a green, resource efficient and circular economy. In contrast, excluding women from this transition could lead to an even greater gender gap of labour participation in the “greener” sectors and economic activities of the future. Achieving a shift in today’s paradigm would require fundamental changes in women’s position in the labour market.

Existing obstacles to women’s economic advancement limit their participation in the green economy. Addressing them could shift this trend. First, women today are less likely to occupy full-time positions and open-ended contracts; they are paid less than men for the same job; and they face greater barriers to getting promoted due to discrimination social norms (e.g. childcare, household upkeep), and conscious and unconscious biases (OECD, 2017[120]). In many countries, women also have greater difficulty accessing finance, thereby reducing their chances of becoming entrepreneurs or developing their business (OECD, 2016[121]).

A second factor relates to women’s and girls’ education and skills. Green economy jobs tend to be high-skilled jobs and are expected to be even more so in the future, requiring specific technical expertise (OECD, 2012[112]). Educational backgrounds in STEM subjects and natural sciences are priced in the innovative and technology-rich green sector. Yet, from an early age, it is mostly boys who more often choose a career in science and engineering, despite the fact that girls also score highly in the PISA tests (OECD, 2020[122]). The percentage of women participating (working as professionals and technicians) in technology development (inventive activity) remains low, reaching only 15% on average across all countries and all technology domains (OECD, 2017[123]). There is a relatively higher participation observed for chemistry and health-related technologies (20% and 24% respectively), while environment-related technologies are just below the average participation, and the rate is even lower for power generation and general engineering technologies (10% and 8% respectively (OECD, 2021[124]).Addressing education gaps of girls studying STEM subjects should therefore be a key part of any strategy to boost female employment and prevent their being left out of the green, low carbon transition.

3.5. Women’s role in accelerating the shift towards sustainable consumption patterns

The transition to a low carbon, green economy requires not only a shift towards sustainable production but also a change in consumption patterns, both for end-consumers and for small and medium sized enterprises (SMEs). Indisputably, consumption patterns are highly dependent on socio-economic factors, income level, race, geography, behaviour etc. They are often dependent on sustainable and social infrastructure (as in the case of transport, education and health expenses for the household); or on policy measures such as pricing, environmental taxation and subsidies, which all influence end-consumers’ preferences (Sharma, Nguyen and Grote, 2018[125]); (Noël, 2018[126]).

Gender appears to be an important factor, which influences consumption behaviour and patterns at the individual level. Several studies mark the social and/or behavioural reasoning behind such attitudes (Bharti and Faust, 2020[127]); (Mirosa, 2014[128]). The 2011 OECD household survey indicated differences between the consumption preferences of men and women, based on participants own responses. These differences occur in terms of the importance given to pressing environmental issues and in terms of consumption preferences, such as energy saving (OECD, 2011[129]) (see more on this topic under Chapter 11).

Toro, Serrano and Guillen (2019) estimated the gendered environmental footprints generated from private consumption (Toro, Serrano and Guillen, 2019[130]). Using single-person households in Spain for their research, they calculated that, for the period 2008-2013, male households generated more GHG emissions than female ones, despite the fact that in total, there is a decrease in the GHG emissions produced by Spanish single-person households. Women’s carbon footprints come from consuming “food and non-alcoholic beverages”, “clothing and footwear” and “rentals and supplies”. Conversely, men’s carbon footprints are higher, coming from the purchase and use of personal vehicles. When considering age, men under 50 seem to generate more GHG emissions than women of the same age group. The consumption ratio is inversed for men and women over 50. If expenditure level is another variable besides GHG footprint, then women appear to generate more GHG emissions than men.

3.6. The gender-environment nexus in economic accounting and well-being frameworks

For some time, economists and statisticians have been working to develop integrated economic measurement and analytical frameworks that incorporate economic, social and environmental considerations. Currently the UN is leading work on the so-called System of Environmental-Economic Accounting (SEEA), which aims to integrate economic, environmental and social data into a single, coherent framework for holistic decision-making (UN, n.d.[131]). The SEEA framework follows a similar accounting structure to the System of National Accounts (SNA) (UN, n.d.[132]). The SEEA Central Framework was adopted by the UN Statistical Commission as the first international standard for environmental-economic accounting in 2012.

Such initiatives on national statistics should eventually allow the development of new composite macro indicators that would complement the GDP as a measure of economic development with information on net environmental value created. As such, it presents an opportunity to incorporate the role of non-market transactions, including the contribution of women to sustainable development through non-remunerated household and community work.

A second important strand of the measurement agenda relates to non-material measures of well-being; which are already integrated into the OECD Well-being Framework. These have also been applied to a recent OECD report where climate change mitigation policies in specific sectors are viewed under a well-being lens (OECD, 2019[98]). Such measures, which cover quality of life aspects (e.g. health, knowledge and skills, safety) and relational aspects (e.g. social connections, work-life balance, civic engagement) complement the material aspects (also essential to people’s well-being). All these aspects constitute the ingredients for a good life and show what people themselves value the most.

References

[34] Abdo, M. et al. (2011), “Impact of Urban Air Pollution on Acute Upper Respiratory Tract Infections”, in Advanced Topics in Environmental Health and Air Pollution Case Studies, InTech, https://doi.org/10.5772/17085.

[90] Achebak, H., D. Devolder and J. Ballester (2019), “Trends in temperature-related age-specific and sex-specific mortality from cardiovascular diseases in Spain: a national time-series analysis”, The Lancet Planetary Health, Vol. 3/7, pp. e297-e306, https://doi.org/10.1016/s2542-5196(19)30090-7.

[105] Adams-Prassl, A. et al. (2020), Inequality in the Impact of the Coronavirus Shock: Evidence from Real Time Surveys, http://ftp.iza.org/dp13183.pdf.

[108] Agénor, P. and M. Agénor (2014), “Infrastructure, women’s time allocation, and economic development”, Journal of Economics/ Zeitschrift fur Nationalokonomie, Vol. 113/1, pp. 1-30, https://doi.org/10.1007/s00712-013-0358-0.

[30] Agrawal, M. et al. (2003), “Effect of air pollution on peri-urban agriculture: a case study”, Environmental Pollution, Vol. 126/3, pp. 323-329, https://doi.org/10.1016/s0269-7491(03)00245-8.

[78] Ahmed, S. et al. (2004), Health and Safety in the Textile Dyeing Industry, https://assets.publishing.service.gov.uk/media/57a08cc9e5274a27b2001415/R8161-Safety.pdf.

[87] Alberini, A. et al. (2010), Valuation of Environment-Related Health Risks for Children, OECD, https://doi.org/10.1787/9789264038042-en.

[106] Alon, T. et al. (2020), The Impact of COVID-19 on Gender Equality, National Bureau of Economic Research, Cambridge, MA, https://doi.org/10.3386/w26947.

[18] Aragón, F., J. Miranda and P. Oliva (2017), “Particulate matter and labor supply: The role of caregiving and non-linearities”, Journal of Environmental Economics and Management, Vol. 86, pp. 295-309, https://doi.org/10.1016/j.jeem.2017.02.008.

[76] Arbuckle, T. (2006), “Are there sex and gender differences in acute exposure to chemicals in the same setting?”, Environmental Research, Vol. 101/2, pp. 195-204, https://doi.org/10.1016/j.envres.2005.08.015.

[11] Balestra, C. and J. Sultan (2013), “Home Sweet Home: The Determinants of Residential Satisfaction and its Relation with Well-being”, OECD Statistics Working Papers, No. 2013/5, OECD Publishing, Paris, https://dx.doi.org/10.1787/5jzbcx0czc0x-en.

[71] Bergman, A., J. Rüegg and E. Drakvik (2019), Integrating Epidemiology and Experimental Biology to Improve Risk Assessment of Exposure to Mixtures of Endocrine Disruptive Compounds, ECD-MixRisk, https://edcmixrisk.ki.se/wp-content/uploads/sites/34/2019/09/EDC-MixRisk_Final-technical-report_f20190629.pdf.

[127] Bharti, M. and N. Faust (2020), The Impact of Gender and Culture in Consumer Behavior, Cambridge University Press., https://www.cambridge.org/core/books/cambridge-handbook-of-the-international-psychology-of-women/impact-of-gender-and-culture-in-consumer-behavior/CD2E91FC3CF739FB5D7EBBF1E8A6DEEA.

[49] Bondy, M., S. Roth and L. Sager (2018), “Crime is in the Air: The Contemporaneous Relationship between Air Pollution and Crime”, Institute of Labor Economics, http://ftp.iza.org/dp11492.pdf.

[19] Bové, H. et al. (2019), “Ambient black carbon particles reach the fetal side of human placenta”, Nature Communications, Vol. 10/1, https://doi.org/10.1038/s41467-019-11654-3.

[46] Burkhardt, J. et al. (2019), “The effect of pollution on crime: Evidence from data on particulate matter and ozone”, Journal of Environmental Economics and Management, Vol. 98, p. 102267, https://doi.org/10.1016/j.jeem.2019.102267.

[28] Butter, M. (2006), “Are Women More Vulnerable to Environmental Pollution?”, Journal of Human Ecology, Vol. 20/3, pp. 221-226, https://doi.org/10.1080/09709274.2006.11905931.

[21] Carrington, D. (2019), Air Pollution ’As Bad as Smoking in Increasing Risk of Miscarriage’”,, https://www.theguardian.com/environment/2019/jan/11/air-pollution-as-bad-as-smoking-in-increasing-risk-of-miscarriage.

[88] Cascajo, R., A. Garcia-Martinez and A. Monzon (2017), “Stated preference survey for estimating passenger transfer penalties: design and application to Madrid”, European Transport Research Review, Vol. 9/3, https://doi.org/10.1007/s12544-017-0260-x.

[114] Chateau, J., R. Bibas and E. Lanzi (2018), “Impacts of Green Growth Policies on Labour Markets and Wage Income Distribution: A General Equilibrium Application to Climate and Energy Policies”, OECD Environment Working Papers, No. 137, OECD Publishing, Paris, https://dx.doi.org/10.1787/ea3696f4-en.

[119] Chateau, J. and E. Mavroeidi (2020), “The jobs potential of a transition towards a resource efficient and circular economy”, OECD Environment Working Papers, No. 167, OECD Publishing, Paris, https://dx.doi.org/10.1787/28e768df-en.

[31] Chay, K. and M. Greenstone (2003), “The Impact of Air Pollution on Infant Mortality: Evidence from Geographic Variation in Pollution Shocks Induced by a Recession”, The Quarterly Journal of Economics, Vol. 118/3, pp. 1121-1167, https://doi.org/10.1162/00335530360698513.

[44] Chen, X., X. Zhang and X. Zhang (2017), “Smog in Our Brains: Gender Differences in the Impact of Exposure to Air Pollution on Cognitive Performance”, IZA Institute of Labour Economics 10628, http://ftp.iza.org/dp10628.pdf.

[107] Clancy, J., M. Skutsch and S. Batchelor (2003), The Gender-Energy-Poverty Nexus: Finding the Energy to Address Gender Concerns in Development, https://www.researchgate.net/publication/237232946_The_Gender-Energy-Poverty_Nexus_Finding_the_Energy_to_Address_Gender_Concerns_in_Development.

[38] Cui, Y. et al. (2003), “Air pollution and case fatality of SARS in the People’s Republic of China: an ecologic study”, Environmental Health, Vol. 2/1, https://doi.org/10.1186/1476-069x-2-15.

[57] Currie, J. et al. (2013), “Something in the water: contaminated drinking water and infant health”, Canadian Journal of Economics/Revue canadienne d’économique, Vol. 46/3, pp. 791-810, https://doi.org/10.1111/caje.12039.

[20] Currie, J. and M. Neidell (2004), Air Pollution and Infant Health: What Can We Learn From California’s Recent Experience, National Bureau of Economic Research, Cambridge, MA, https://doi.org/10.3386/w10251.

[75] Czaja, S. et al. (2006), “Factors predicting the use of technology: Findings from the center for research and education on aging and technology enhancement (create).”, Psychology and Aging, Vol. 21/2, pp. 333-352, https://doi.org/10.1037/0882-7974.21.2.333.

[25] Dechezleprêtre, A., N. Rivers and B. Stadler (2019), “The economic cost of air pollution: Evidence from Europe”, OECD Economics Department Working Papers, No. 1584, OECD Publishing, Paris, https://dx.doi.org/10.1787/56119490-en.

[40] EIGE (2020), “Beijing +25: the fifth review of the implementation of the Beijing Platform for Action in the EU Member States”, https://eige.europa.eu/publications/beijing-25-fifth-review-implementation-beijing-platform-action-eu-member-states.

[96] EIGE (2020), Gender Equality index 2019 Work-Life Balance, https://doi.org/10.2839/852588.

[101] EmPower (2020), The Impact of COVID-19 on Rural Women and Enterprises: A Rapid Socio-Economic Assessment in Viet Nam by the EmPower Project, Sida, UN Enviornment Programme, UN Women, https://reliefweb.int/sites/reliefweb.int/files/resources/The%20impact%20of%20COVID-19%20on%20rural%20women%20and%20enterprises%20-%20A%20rapid%20socio-economic%20assessment%20in%20Viet%20Nam%20by%20the%20empower%20project.pdf (accessed on 30 November 2020).

[115] FAO (2011), The State of Food and Agriculture. Women in Agriculture: Closing the Gender Gap for Development, Food and Agriculture Organization of the United Nations, Rome, http://www.fao.org/publications/sofa/2010-11/en/.

[50] Fauconnier, I., A. Jenniskens and P. Perry (2018), Women as change-makers in the governance of shared waters, IUCN, International Union for Conservation of Nature, https://doi.org/10.2305/iucn.ch.2018.22.en.

[89] Fouillet, A. et al. (2006), “Excess mortality related to the August 2003 heat wave in France”, International Archives of Occupational and Environmental Health, Vol. 80/1, pp. 16-24, https://doi.org/10.1007/s00420-006-0089-4.

[24] Gauderman, W. et al. (2007), “Effect of exposure to traffic on lung development from 10 to 18 years of age: a cohort study”, The Lancet, Vol. 369/9561, pp. 571-577, https://doi.org/10.1016/s0140-6736(07)60037-3.

[3] GBD (2019), Global Burden of Disease Study 2019 Results, Institute for Health Metrics and Evaluation, Seattle, http://ghdx.healthdata.org/gbd-results-tool.

[29] Girard, P. (2002), Xharcoal Production and Use in Africa: What Future?, https://www.researchgate.net/publication/283832140_Charcoal_production_and_use_in_Africa_What_future.

[80] Hertz-Picciotto, I. et al. (2010), “Blood Mercury Concentrations in CHARGE Study Children with and without Autism”, Environmental Health Perspectives, Vol. 118/1, pp. 161-166, https://doi.org/10.1289/ehp.0900736.

[73] Hohenadel, K. et al. (2015), “The Inclusion of Women in Studies of Occupational Cancer: A Review of the Epidemiologic Literature From 1991-2009”, American Journal of Industrial Medicine, Vol. 58/3, pp. 276-281, https://doi.org/10.1002/ajim.22424.

[77] ILO (2019), The Future of Work in Textiles , Clothing, Leather and Footwear, ILO, https://www.ilo.org/sector/Resources/publications/WCMS_669355/lang--en/index.htm.

[117] ILO (2016), “What is a Green Job?”, https://www.ilo.org/global/topics/green-jobs/news/WCMS_220248/lang--en/index.htm.

[133] ILO (2000), ABC for Women Workers’ Rights and Gender Equality, International Labour Office, Geneva, https://www.ilo.org/wcmsp5/groups/public/---dgreports/---gender/documents/publication/wcms_087314.pdf (accessed on 20 October 2020).

[12] Inyinbor, A. et al. (2018), “Water Pollution: Effects, Prevention, and Climatic Impact”, in Water Challenges of an Urbanizing World, InTech, https://doi.org/10.5772/intechopen.72018.

[64] IPBES (2019), Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, https://ipbes.net/global-assessment.

[118] IRENA (2019), Renewable Energy: A Gender Perspective, IRENA, https://www.irena.org/publications/2019/Jan/Renewable-Energy-A-Gender-Perspective.

[99] ITF (2020), Re-spacing Our Cities for Resilience: Covid-19 Transport Brief, OECD/ITF, https://www.itf-oecd.org/sites/default/files/respacing-cities-resilience-covid-19.pdf.

[26] ITF (2018), Understanding Urban Travel Behaviour by Gender for Efficient and Equitable Transport Policies, OECD Publishing, Paris, https://doi.org/10.1787/eaf64f94-en.

[97] ITF (2018), Women’s Safety and Security, OECD/ITF, https://www.itf-oecd.org/womens-safety-security.

[109] Kabeer, N. (2012), Women’s Economic Empowerment and Inclusive Growth: Labour Markets and Enterprise Development, https://www.lse.ac.uk/gender/assets/documents/research/choice-constraints-and-the-gender-dynamics-of-lab/Women%27s-economic-empowerment-and-inclusive-growth.pdf.

[42] Kioumourtzoglou, M. et al. (2017), “The Association Between Air Pollution and Onset of Depression Among Middle-Aged and Older Women”, American Journal of Epidemiology, Vol. 185/9, pp. 801-809, https://doi.org/10.1093/aje/kww163.

[32] Kovačićek, T. and R. R. Franić (2019), “The Professional Status of Rural Women in the EU”, European Parliament, https://www.europarl.europa.eu/RegData/etudes/STUD/2019/608868/IPOL_STU(2019)608868_EN.pdf.

[60] Lal, R. (ed.) (2020), Soil and Fertilizers, CRC Press, https://doi.org/10.1201/9780429471049.

[51] Landrigan, P. et al. (2018), “The Lancet Commission on pollution and health”, The Lancet, Vol. 391/10119, pp. 462-512, https://doi.org/10.1016/s0140-6736(17)32345-0.

[67] Lemonsu, A. et al. (2015), “Vulnerability to heat waves: Impact of urban expansion scenarios on urban heat island and heat stress in Paris (France)”, Urban Climate, Vol. 14, pp. 586-605, https://doi.org/10.1016/j.uclim.2015.10.007.

[63] Lovett, G. et al. (2009), “Effects of Air Pollution on Ecosystems and Biological Diversity in the Eastern United States”, Annals of the New York Academy of Sciences, Vol. 1162/1, pp. 99-135, https://doi.org/10.1111/j.1749-6632.2009.04153.x.

[47] Lu, J. et al. (2018), “Polluted Morality: Air Pollution Predicts Criminal Activity and Unethical Behavior”, Psychological Science, Vol. 29/3, pp. 340-355, https://doi.org/10.1177/0956797617735807.

[85] Lynn, H., M. Mantingh and S. Rech (2017), Plastic, Gender and the Environment. Findings of a Literature Study on the Lifecycle of Plastics and its Impacts on Women and Men, from Production to Litter, https://doi.org/DOI:10.13140/RG.2.2.33644.26242.

[7] Manisalidis, I. et al. (2020), “Environmental and Health Impacts of Air Pollution: A Review”, Frontiers in Public Health, Vol. 8, https://doi.org/10.3389/fpubh.2020.00014.

[68] McGregor, G. (2015), Heatwaves and Health:: Guidance on Warning-System Development, World Health Organization (WHO), https://www.who.int/globalchange/publications/WMO_WHO_Heat_Health_Guidance_2015.pdf.

[128] Mirosa, M. (2014), “Sustainable Consumption and Gender”, in Encyclopedia of Food and Agricultural Ethics, Springer Netherlands, Dordrecht, https://doi.org/10.1007/978-94-007-0929-4_461.

[102] Moffitt, P. et al. (2020), “Intimate Partner Violence and COVID-19 in Rural, Remote, and Northern Canada: Relationship, Vulnerability and Risk”, Journal of Family Violence, https://doi.org/10.1007/s10896-020-00212-x.

[33] Montt, G. (2018), “Too polluted to work? The gendered correlates of air pollution on hours worked”, IZA Journal of Labor Economics, Vol. 7/1, https://doi.org/10.1186/s40172-018-0067-6.

[58] Mrema, E. et al. (2017), “Pesticide Exposure and Health Problems Among Female Horticulture Workers in Tanzania”, Environmental Health Insights, Vol. 11, p. 117863021771523, https://doi.org/10.1177/1178630217715237.

[45] Nickerson, S. (2019), “Air Pollution Exposure Is Linked to Increased Violent Crime, Domestic Violence”, EcoWatch, https://www.ecowatch.com/air-pollution-violent-crime-domestic-2640927925.html?rebelltitem=1#rebelltitem1.

[126] Noël, R. (2018), Race, Economics, and Social Status, U.S. Bureau Labour Statistics, https://www.bls.gov/spotlight/2018/race-economics-and-social-status/pdf/race-economics-and-social-status.pdf.

[4] OECD (2021), Mortality, Morbidity and Welfare Cost from Exposure to Environment-related Risks, OECD, Paris, https://stats.oecd.org/Index.aspx?QueryId=90835#.

[124] OECD (2021), OECD Environment Statistics, https://www.oecd-ilibrary.org/environment/data/oecd-environment-statistics_env-data-en#:~:text=relevant%20environmental%20statistics.-,The%20OECD%20Environmental%20Statistics%20database%20provide%20a,of%20policy%2Drelevant%20environmental%20statistics.&te.

[113] OECD (2020), COVID-19 and the Low-carbon Transition: Impacts and Possible Policy Responses, OECD Publishing, Paris, https://www.oecd.org/coronavirus/policy-responses/covid-19-and-the-low-carbon-transition-impacts-and-possible-policy-responses-749738fc/.

[122] OECD (2020), Dream Jobs? Teenagers’ Career Aspirations and the Future of Work, OECD Publishing, Paris, https://www.oecd.org/berlin/publikationen/Dream-Jobs.pdf.

[2] OECD (2020), How’s Life? 2020: Measuring Well-being, OECD Publishing, Paris, https://dx.doi.org/10.1787/9870c393-en.

[6] OECD (2020), Mortality, Morbidity and Welfare Cost From Exposure to Environment-Related Risks, OECD, Paris, https://stats.oecd.org/FileView2.aspx?IDFile=1356df09-6bd4-4b0b-9a95-d56e145769ad.

[116] OECD (2020), Who Cares? Attracting and Retaining Elderly Care Workers, OECD, https://doi.org/10.1787/92c0ef68-en.

[104] OECD (2020), Women at the Core of the Fight against COVID-19 Crisis, OECD Publishing, Paris, https://read.oecd-ilibrary.org/view/?ref=127_127000-awfnqj80me&title=Women-at-the-core-of-the-fight-against-COVID-19-crisis.

[98] OECD (2019), Accelerating Climate Action, OECD, https://doi.org/10.1787/2f4c8c9a-en.

[56] OECD (2019), Pharmaceutical Residues in Freshwater: Hazards and Policy Responses, OECD Publishing, Paris.

[103] OECD (2018), Bridging the digital Gender Divide: Include, Upskill Innovate, OECD Publishing, Paris, https://www.oecd.org/going-digital/bridging-the-digital-gender-divide-key-messages.pdf.

[66] OECD (2018), Considerations for Assesing the Risks of Combined Exposure to Multiple Chemicals, https://www.oecd.org/chemicalsafety/risk-assessment/considerations-for-assessing-the-risks-of-combined-exposure-to-multiple-chemicals.pdf.

[84] OECD (2018), Improving Plastics Management: Trends, Policy Responses, and the Role of International Co-operation and Trade, OECD Publishing, Paris, https://www.oecd.org/environment/waste/policy-highlights-improving-plastics-management.pdf.

[110] OECD (2018), Is the Last Mile the Longest? Economic Gains from Gender Equality in Nordic Countries, OECD, https://doi.org/10.1787/9789264300040-en.

[79] OECD (2018), OECD Due Diligence Guidance for Responsible Supply Chains in the Garment and Footwear Sector, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264290587-en.

[86] OECD (2018), Presentations from OECD Workshop on the Best Practices in Assessing the Soical Costs of Selected Chemicals, https://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=env/jm/mono(2018)22/ann1&doclanguage=en.

[123] OECD (2017), Going Digital: The Future of Work for Women, https://www.oecd.org/employment/Going-Digital-the-Future-of-Work-for-Women.pdf.

[120] OECD (2017), Meeting of the OECD Council at Ministerial Level - Report on the Implementation of the OECD Gender Recommendations, http://www.oecd.org/mcm-2018/documents/C-MIN-2017-7-EN.pdf.

[121] OECD (2016), Entrepreneurship at a Glance 2016, OECD, https://doi.org/10.1787/entrepreneur_aag-2016-en.

[9] OECD (2016), The Economic Consequences of Outdoor Air Pollution, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264257474-en.

[17] OECD (2014), The Cost of Air Pollution, OECD, https://doi.org/10.1787/9789264210448-en.

[82] OECD (2013), OECD Guidelines for the Testig of Chemicals, OECD Publishing, Paris, https://www.oecd-ilibrary.org/environment/oecd-guidelines-for-the-testing-of-chemicals_72d77764-en.

[16] OECD (2012), Environmental Quality and Life Satisfaction: Evidence on Microdata, OECD Publishing, Paris, https://doi.org/10.1787/19970900.

[10] OECD (2012), OECD Environmental Outlook to 2050: The Consequences of Inaction, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264122246-en.

[112] OECD (2012), “The Jobs Potential of a Shift Towards a Low-Carbon Economy”, OECD Green Growth Papers, No. 2012/1, OECD Publishing, Paris, https://dx.doi.org/10.1787/5k9h3630320v-en.

[129] OECD (2011), Greening Household Behaviour, OECD, https://doi.org/10.1787/9789264096875-en.

[14] Okello, G., G. Devereux and S. Semple (2018), “Women and girls in resource poor countries experience much greater exposure to household air pollutants than men: Results from Uganda and Ethiopia”, Environment International, Vol. 119, pp. 429-437, https://doi.org/10.1016/j.envint.2018.07.002.

[92] Ortiz, C. et al. (2017), “Evaluation of short-term mortality attributable to particulate matter pollution in Spain”, Environmental Pollution, Vol. 224, pp. 541-551, https://doi.org/10.1016/j.envpol.2017.02.037.

[111] Ostry, J. et al. (2018), Economic Gains From Gender Inclusion : New Mechanisms, New Evidence, IMF, https://www.imf.org/en/Publications/Staff-Discussion-Notes/Issues/2018/10/09/Economic-Gains-From-Gender-Inclusion-New-Mechanisms-New-Evidence-45543.

[65] Papadopoulou, M. et al. (2019), “TCR Sequencing Reveals the Distinct Development of Fetal and Adult Human Vγ9Vδ2 T Cells”, The Journal of Immunology, Vol. 203/6, pp. 1468-1479, https://doi.org/10.4049/jimmunol.1900592.

[95] Phadera, L. (2016), International Migration and its Effect on Labor Supply of the Left-Behind Household Members: Evidence from Nepal, Agricultural and Applied Economics Association, https://doi.org/10.22004/ag.econ.235968.

[8] Prüss-Üstün, A., C. Corvalán and WHO (2006), Preventing Disease Through Healthy Environments : Towards an Estimate of the Environmental Burden of Disease, https://apps.who.int/iris/handle/10665/43457.

[59] Roser, M. and H. Ritchie (2020), Fertilizers, Our World in Data, https://ourworldindata.org/fertilizers.

[41] Roy, R. (2016), “The cost of air pollution in Africa”, OECD Development Centre Working Papers, No. 333, OECD Publishing, Paris, https://dx.doi.org/10.1787/5jlqzq77x6f8-en.

[5] Roy, R. and N. Braathen (2017), “The Rising Cost of Ambient Air Pollution thus far in the 21st Century: Results from the BRIICS and the OECD Countries”, OECD Environment Working Papers, No. 124, OECD Publishing, Paris, https://dx.doi.org/10.1787/d1b2b844-en.

[22] Saha, S. et al. (2007), “Cigarette smoke and adverse health effects: An overview of research trends and future needs”, International Journal of Angiology, Vol. 16/03, pp. 77-83, https://doi.org/10.1055/s-0031-1278254.

[100] Salcedo-La Viña, C., R. Singh and N. Elwell (2020), Rural Women Must Be at the Heart of COVID-19 Response and Recovery, World Resources Institute, https://www.wri.org/blog/2020/09/rural-women-must-be-heart-covid-19-response-and-recovery (accessed on 30 November 2020).

[74] Scarselli, A. et al. (2018), “Gender differences in occupational exposure to carcinogens among Italian workers”, BMC Public Health, Vol. 18/1, https://doi.org/10.1186/s12889-018-5332-x.

[37] Setti, L. et al. (2020), SARS-Cov-2 RNA Found on Particulate Matter of Bergamo in Northern Italy: First Preliminary Evidence, Cold Spring Harbor Laboratory, https://doi.org/10.1101/2020.04.15.20065995.

[36] Setti, L. et al. (2020), The Potential role of Particulate Matter in the Spreading of COVID-19 in Northern Italy: First Evidence-based Research Hypotheses, Cold Spring Harbor Laboratory, https://doi.org/10.1101/2020.04.11.20061713.

[27] Setton, E. et al. (2010), “Gender Differences in Chronic Exposure to Traffic-Related Air Pollution—A Simulation Study of Working Females and Males”, The Professional Geographer, Vol. 62/1, pp. 66-83, https://doi.org/10.1080/00330120903404728.

[125] Sharma, R., T. Nguyen and U. Grote (2018), “Changing Consumption Patterns—Drivers and the Environmental Impact”, Sustainability, Vol. 10/11, p. 4190, https://doi.org/10.3390/su10114190.

[69] Street, M. et al. (2018), “Current Knowledge on Endocrine Disrupting Chemicals (EDCs) from Animal Biology to Humans, from Pregnancy to Adulthood: Highlights from a National Italian Meeting”, International Journal of Molecular Sciences, Vol. 19/6, p. 1647, https://doi.org/10.3390/ijms19061647.

[23] Suissa, S. and M. Edwardes (1997), “Adjusted Odds Ratios for Case-Control Studies with Missing Confounder Data in Controls”, Epidemiology, Vol. 8/3, p. 275, https://doi.org/10.1097/00001648-199705000-00008.

[83] Ten Brink, P. et al. (2016), Plastics Marine Litter and the Circular Economy, IEEP.

[130] Toro, F., M. Serrano and M. Guillen (2019), “Who pollutes more? Gender Differences in Consumption Patterns, Research Institute of Applied Economics, https://www.ub.edu/irea/working_papers/2019/201906.pdf.

[48] Truman, J. and R. Morgan (2014), “Nonfatal Domestic Violence, 2003-2012”, Bureau of Justice Statistics, https://www.bjs.gov/content/pub/pdf/ndv0312.pdf.

[131] UN (n.d.), System of Environmental-Economic Accounting (SEEA), United Nations (UN), https://seea.un.org/.

[132] UN (n.d.), System of National Accounts (SNA), United Nations (UN), https://unstats.un.org/unsd/nationalaccount/sna.asp.

[94] UN Women (2018), Turning promises into action: Gender equality in the 2030 Agenda for Sustainable Development, https://www.unwomen.org/en/digital-library/publications/2018/2/gender-equality-in-the-2030-agenda-for-sustainable-development-2018.

[61] UNDP (2011), Chemicals and Gender Energy Map: Environment Practice Gender Mainstreaming Guidance Series Chemicals Management., https://www.undp.org/content/dam/aplaws/publication/en/publications/environment-energy/www-ee-library/chemicals-management/chemicals-and-gender/2011%20Chemical&Gender.pdf.

[62] UNEP (2013), Women and Natural Resources : Unlocking the Peacebuilding Potential...

[93] UNEP (2011), Women at the Frontline of Climate Change: Gender Risks and Hopes, http://hdl.handle.net/20.500.11822/7985.

[1] UNHCR (2019), Global Trends: Forced Displacement in 2018, UN High Commissioner for Refugees (UNHCR), https://www.unhcr.org/statistics/unhcrstats/5d08d7ee7/unhcr-global-trends-2018.html.

[72] UNSD (n.d.), SDG Indicators. United Nations Global SDG Database, https://unstats.un.org/sdgs/indicators/database/ (accessed on 20 October 2020).

[54] Watts, N. et al. (2019), “The 2019 report of The Lancet Countdown on health and climate change: ensuring that the health of a child born today is not defined by a changing climate”, The Lancet, Vol. 394/10211, pp. 1836-1878, https://doi.org/10.1016/s0140-6736(19)32596-6.

[13] WHO (2016), “Preventing Disease Through Healthy Environments: A global Assessment of the Burden of Disease from Environmental Risks”, https://www.who.int/quantifying_ehimpacts/publications/preventing-disease/en/.

[70] WHO (2016), The Public Health Impact of Chemicals: Knowns and Unknowns, World Health Organization (WHO), https://apps.who.int/iris/handle/10665/206553.

[52] WHO (2014), “Preventing Diarrhoea Through Better Water, Sanitation and Hygiene: Exposures and impacts in low=and middle-income countries”, World Health Organization.

[53] WHO and UNICEF (2017), “Progress on Drinking Water, Sanitation and Hygiene: Joint Monitoring Programme 2017 update and SDG baselines”, https://www.who.int/water_sanitation_health/publications/jmp-2017/en/.

[55] Woodcock, J. et al. (2009), “Public health benefits of strategies to reduce greenhouse-gas emissions: urban land transport”, The Lancet, Vol. 374/9705, pp. 1930-1943, https://doi.org/10.1016/s0140-6736(09)61714-1.

[15] World Health Organisation (ed.) (2016), Burning Opportunity: Clean Household Energy for Health, Sustainable Development, and Wellbeing of Women and Children, https://www.who.int/airpollution/publications/burning-opportunities/en/.

[35] Wu, X. et al. (2020), “Exposure to air pollution and COVID-19 mortality in the United States”, https://doi.org/10.1101/2020.04.05.20054502.

[43] Xin, Z., Z. Xiaobo and C. Xi (2015), “Happines in the Air: How Does a Dirty Sky Affect Subjective Well-Being?”, ZA – Institute of Labor Economics, https://www.econstor.eu/bitstream/10419/120969/1/dp9312.pdf.

[91] Yin, Q. et al. (2019), “Mapping the increased minimum mortality temperatures in the context of global climate change”, Nature Communications, Vol. 10/1, https://doi.org/10.1038/s41467-019-12663-y.

[39] Zhonghua, L., B. Xing and X. Z (2020), “The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China”, Epidemiology Working Group for NCIP Epidemic Response, Chinese Center for Disease Control and Prevention., https://doi.org/10.3760/cma.j.issn.0254-6450.2020.02.003.

[81] Zota, A. and B. Shamasunder (2017), “The environmental injustice of beauty: framing chemical exposures from beauty products as a health disparities concern”, American Journal of Obstetrics and Gynecology, Vol. 217/4, pp. 418.e1-418.e6, https://doi.org/10.1016/j.ajog.2017.07.020.

Notes

← 1. Gender equality and gender equity are two related yet distinct concepts. Based on ILO definitions, “gender equity means fairness of treatment for women and men, according to their respective needs and interests. This may include equal treatment or treatment that is different but considered equivalent in terms of rights, benefits, obligations and opportunities”; “Gender equality refers to the enjoyment of equal rights, opportunities and treatment by men and women and by boys and girls in all spheres of life. It asserts that people’s rights, responsibilities, social status and access to resources do not depend on whether they are born male or female” (ILO, 2000[133]). Although there is a distinction between equality and equity, for ease of reference and simplicity’s sake, this report uses only the term “inequalities”.

← 2. See REACH – Eliminating Toxic chemicals in the EU (https://www.wecf.eu/english/campaigns/2004/reach.php)

← 3. Worth noting that life expectancy for women is larger than for men and that vulnerability to heatwaves increases with age.

← 4. OECD’s statistical definition of infrastructure refers to “the system of public works in a country, state or region, including roads, utility lines and public buildings”. However, the term infrastructure from a policy perspective covers a wider set of systems and services, including infrastructure investment, planning and management; and eventually usage and economic spillovers.

← 5. Social infrastructure refers to infrastructure that supports the development of the human resource potential and ameliorates living conditions. It includes, but is not limited to, infrastructure relating to education; health; and water supply, sanitation and sewerage.

╳

Metadata, Legal and Rights

This document, as well as any data and map included herein, are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. Extracts from publications may be subject to additional disclaimers, which are set out in the complete version of the publication, available at the link provided.

https://doi.org/10.1787/3d32ca39-en

The use of this work, whether digital or print, is governed by the Terms and Conditions to be found at http://www.oecd.org/termsandconditions.