Serious haze can cause contaminant diseases that trigger productive
labour time by raising mortality and morbidity rates in cardiovascular and
respiratory diseases. Health studies rarely consider macroeconomic impacts of
industrial interlinkages while disaster studies seldom involve air pollution
and its health consequences. This study adopts a supply-driven input–output
model to estimate the economic loss resulted from disease-induced
working-time reduction across 30 Chinese provinces in 2012 using the most
updated Chinese multiregional input–output table. Results show a total
economic loss of CNY 398.23 billion (
Millions of people in China are currently breathing a toxic cocktail of chemicals, which has become one of the most serious environmental issues in China resulting in widespread environmental and health problems (Meng et al., 2015, 2016a), including increasing risks for heart and respiratory diseases, stroke, and lung cancer. As air pollution has long-term health impacts that evolve gradually over time, understanding the health and socioeconomic impacts of China's air pollution requires continuous efforts.
Serious air pollution in China has largely inspired epidemic studies that examine specific health outcomes from air pollution as well as health cost assessments that translate health outcomes into monetary loss (Xu et al., 2000; Venners et al., 2003; Kan and Chen, 2004). Existing epidemic studies simulate an exposure–response relationship between particulate matter (PM) concentration levels and relative risks (RRs) for a particular disease (see Wong et al., 1999, 2002; Xu et al., 2000; Venners et al., 2003), while health cost assessments frequently stem from patients' perspectives at microeconomic level, by evaluating either their willingness-to-pay (WTP) to avoid disease risk (see Wang and Mullahy, 2006; Wang et al., 2006; Zeng and Jiang, 2010) or the potentially productive years of life loss (PPYLL) (see Wan et al., 2005; Miraglia et al., 2005; Mcghee et al., 2006; Bradley et al., 2007). However, when perceiving unhealthy labourers as a degradation in labour input, macroeconomic implications for production supply chains lack investigation. While traditional approaches for health cost estimates are able to provide more information on economic loss from a standpoint of individual patients, we suggest that they are likely to lose sight on the cascading effects due to labour time loss across interrelating industries. Meanwhile, as the health effects of air pollution are slowly built up over time, implying the lasting nature of air pollution, it has been rarely studied in current disaster risk literature. Differing from rapid-onset disaster analyses (flood, hurricane, earthquake, etc.) that normally rely on quantifying damages to physical capital, air pollution affects human capital more than physical capital, and the resulting health impacts are relatively invisible and unmeasurable. As a result, linking PM concentrations with health endpoints and further with macroeconomic impacts requires an interdisciplinary approach that integrates all three of the elements into one. Inspired by our previous work on the socioeconomic impacts of China's air pollution in 2007 (Xia et al., 2016), this paper applies a similar approach to China's air pollution in 2012 and also examines the cross-regional economic impacts in order to underline the important role of indirect economic loss for the year 2012. In other words, it aims to investigate the overall economic loss resulting from health-induced labour time reduction among all Chinese labourers for year of 2012. Given that the majority of economic loss originates from secondary industries, this paper also specifically analyses the key sectors in secondary industries that account for the greatest proportions of both direct and indirect economic loss in each great region in China. By doing so, future policymakers and researchers could obtain an alternative macroeconomic tool to better conduct cost-benefit analysis for any environmental or climate change related policy design, and to comprehend health cost studies in its macroeconomic side.
Methodological framework.
Figure 1 illustrates the overall methodological framework developed by this study. It involves four main parts that are distinguished by four colours. Detailed methods that connect each part in the flow chart are shown near the arrows.
PM
The following sections present many mathematical symbols, formulas, and
equations. For clarity, matrices are indicated by bold, upright capital
letters (e.g.
We referred to Chinese provincial PM
Epidemic studies on PM
An IER model captures concentration–response relationships with a specific
focus on ischemic heart disease, stroke, chronic obstructive pulmonary
disease, and lung cancer. The relative risk for the
mortality estimation function for the four diseases were shown in Eq. (1).
Then, the calculated RR was converted into an attributable fraction (AF) in
Eq. (2).
For morbidity, we calculated cardiovascular and respiratory hospital
admissions and outpatient visits for all causes using a log-linear response
function. The RRs for each category of morbidity were calculated using
Eq. (4) (Jiang et al., 2015).
Counts of PM
Each labourer is assumed to work 8 h every day and 250 days during 2012. For
PM
Provincial counts of PM
We employed a supply-driven IO model to evaluate the indirect economic loss
due to PM
The basic Leontief IO model (Meng et al., 2018) can be therefore derived in
matrix notation (Eq. 7a and 7b).
At the same time, a supply-driven IO model takes a rotated view of Leontief
IO model that shows an opposite influencing direction between sectors. It
suggests that production in a sector can affect sectors purchasing its
outputs as inputs during their production processes and it has a supply-side
focus. A supply-driven IO model is used to calculate the impact of changes in
primary inputs on sectoral gross production. For a supply-driven IO model,
the basic structure is shown in Eq. (8a) and (8b).
Firstly, regarding the total number of affected labourers and total economic
loss, the total economic loss resulting from PM
Cross-regional economic loss analysis. The diagram demonstrates the interregional economic impacts due to their interdependencies. The left-hand side shows the regional indirect economic loss while the right-hand side denotes the sources for these indirect economic losses. The proportion of regional indirect loss among regional total economic loss is displayed next to each region's name on the left-hand side.
Secondly, concerning economic loss by province, region, and industry at
the provincial level (Fig. 2), the economic loss in the Henan province exceeds that
of the Jiangsu province in 2007 (CNY 55.90 billion), becoming the province
suffering the greatest economic loss at 56.37 billion, accounting for 14 %
of the total economic loss in China. This is followed by Jiangsu province at
CNY 45.32 billion and Shangdong province at CNY 43.23 billion. This is
because all three of the provinces have the largest counts of PM
Additionally, this case study also examined the cross-regional economic losses between the six greater regions in China. As one significant advantage of the input–output model is to capture the industrial and regional interdependencies, it is effective to measure the propagating disaster-induced indirect economic loss along the production supply chain. We traced the cross-regional economic loss due to their interlinkages, such as interregional trade, as shown in Fig. 3. The diagram demonstrates the interregional economic impacts due to their interdependencies. The proportion of regional indirect loss among regional total economic loss is displayed next to each region's name on the left-hand side. Although the majority of regional economic loss came from the direct economic loss that occurred within the region across almost all six of the regions, the Northeast, Eastern China, and the Northwest still entail great indirect economic loss from other regions, which occupies 31 %, 21 %, and 30 % of the total regional economic loss, respectively. In the Northeast, 18 % of its total regional economic loss originated from North China and Mid-South, including CNY 1.84 billion from North China and CNY 1.85 billion from Mid-South. Similarly, the Mid-South is responsible for 9 % of the economic loss in Eastern China at CNY 13.36 billion. It accounts for an even larger proportion of regional economic loss in the Northwest at 13 %. Meanwhile, Eastern China also accounts for another 8 % of the total regional economic loss in Northeast, which amounts to CNY 1.66 billion. Overall, the Mid-South accounts for the largest amount of indirect economic loss in other Chinese regions at CNY 24.65 billion, which is followed by North China and Eastern China at CNY 16.99 and 12.17 billion, respectively. This finding highlights the increasing significance in capturing the industrial and regional interdependencies and indirect economic loss in disaster risk analysis because such interdependencies can largely raise the overall economic loss far beyond the direct economic loss and constitute a noticeable component of total economic loss.
Regional direct and indirect economic loss from secondary sectors. The inner ring denotes the direct economic loss originating from secondary sectors inside the region, while the outer ring stands for the indirect economic loss from secondary sectors in other regions. Percentage shown on the inner ring shows the proportion of direct economic loss regarding total regional economic loss and percentages shown on the outer ring are the proportions of indirect loss from other regions relative to total regional indirect economic loss.
Economic loss from seven industries in secondary sector inside and outside the region. The inner circle shows the economic loss from secondary sector inside the region. The size of circle stands for the different proportions of inner-regional economic loss relative to total regional economic loss. Colours demonstrate economic loss from seven sectors in secondary sector inside the region. Meanwhile, the outer circle indicates the economic loss from secondary sectors outside the region. Economic loss resulting from seven sectors are shown in black and white. Percentages shown on the outer circle are the proportions of indirect loss from other regions relative to total regional indirect economic loss.
As secondary sectors play a vital role in the Chinese economy and entails greatest economic loss among the three industries, we specifically analysed the regional economic loss that directly and indirectly resulting from secondary sectors both inside and outside of a region. Focusing on the secondary sector, Fig. 4 illustrates both direct and indirect economic loss originating from each region and outside the region. As can be seen from the diagram, despite the fact that the majority of economic loss resulting from the secondary sectors originated from inside the region for all six of the greater regions in China, in the Northwest and the Northeast, economic loss attributed to secondary sectors outside the region still constituted a considerable share due to industrial and regional interdependencies. Secondary sectors in the Mid-South, Eastern China, and North China became three major sources for indirect economic loss across all six of the regions. For instance, in the Northwest, economic loss from secondary sectors in the Mid-South, Eastern China, and North China account for 14 %, 6 %, and 6 % of total regional indirect loss from secondary sectors outside the region, at CNY 2.20, 0.99, and 0.90 billion, respectively. Similarly, in the Northeast, economic loss from secondary sectors in these three regions occupy 10 %, 8 %, and 9 % of total regional indirect loss from secondary sectors outside the region, at CNY 1.66, 1.33, and 1.46 billion, respectively. This results from their geographical distance to the Mid-South, Eastern China, and North China, as well as close trade relationships with these three regions. The significant roles of Mid-South and Eastern China in interregional trade have been confirmed earlier by Sun and Peng (2011), where they pointed out the export-oriented nature for trades in Eastern China and the Mid-South, and their close trade relations with Northwest regions with respect to the import of raw materials. Likewise, it is noticeable that indirect economic loss is more likely to come from neighbour-regions, which highlights the possibility that short geographical distances might accelerate interregional trade and strengthen regional interlinkages.
The secondary sector was further broken down into seven industries in order to examine the major economic loss sources among subindustries inside and outside the region. They include coal and mining, manufacturing, fuel processing and chemicals, metal and non-metal, equipment, energy, and construction as displayed in Fig. 5. In North China, the Northwest and the Southwest, most of their indirect economic loss from secondary sectors outside the region came from manufacturing with 27.0 %, 26.7 %, and 22.2 %, respectively. The second largest source in these three regions that accounts for economic loss from secondary sectors in other regions is energy, with the greatest amount occurring in North China at CNY 2.32 billion, followed by the Northwest at CNY 1.29 billion, and the Southwest at CNY 1.26 billion. In contrast, coal and mining accounts for the majority of indirect loss from secondary sectors outside the region for Eastern China, the Mid-South and the Northeast at 37.4 % (CNY 10.83 billion), 33.4 % (CNY 3.65 billion), and 24.4 % (CNY 1.30 billion), respectively. One possible underlying reason is that economies in the Northwest, North China, and the Southwest are mainly dominated by coal and mining but rely on the import of manufacturing products from other regions, whereas Eastern China, the Mid-South, and the Northeast have more prosperous manufacturing industries but tend to heavily depend on imports of raw materials from coal and mining industries in the Northwest, North China, or the Southwest. With regards to the economic loss from secondary sectors inside each region, it shows diversified patterns across the six greater regions. Coal and mining account for the largest part of inner-regional economic loss in North China and the Northwest at 42.4 % and 43.8 %, respectively. Equipment and energy appear to be two major sources for inner-regional economic loss Eastern China and the Southwest, while metal and non-metal and manufacturing constitute considerable proportions in inner-regional economic loss from secondary sectors in the Mid-South, which reach CNY 21.86 and 21.61 billion, occupying 27.4 % and 27.1 %, respectively.
PM
The results are threefold. Firstly, the total economic loss from China's air
pollution during 2012 amounts to CNY 398.23 billion with the majority coming
from Eastern China (39 %) and the Mid-South (30 %). The total economic loss
is equivalent with 1.0 % of China's GDP in 2012, and the total number of
affected labourers rises to 82.19 million. Compared with the study in 2007 (Xia
et al., 2016), although secondary industries remain as the industries which
encountered the most economic loss (80 %), changes can be noticed for economic loss
at the provincial level. Henan and Jiangsu became two provinces that suffered the
greatest economic loss at CNY 56.37 and 45.32 billion, respectively, followed
by Shangdong province with a total economic loss of CNY 43.23 billion. Henan
and Shangdong provinces also have the largest numbers of PM
There are some final remarks for policymakers and researchers here from this typical air pollution issue. On the one hand, given the prosperous interregional trade, policymakers are required to conscientiously consider these increasingly strengthened industrial and regional linkages in climate change mitigation and adaptation policy design based on a better understanding of implications resulting from any climate change-induced health issues at both micro and macroeconomic levels. Meanwhile, sufficient adaptation measures are required to be implemented along with the climate change mitigation strategies in operation. The purpose of this is to expand the economy beyond the regional geography and natural endowment and to release the current reliance on the economy on labour-intensive sectors (Mauricio Mesquita, 2007). On the other hand, researchers of epidemic studies should actively integrate these interdependencies into future health cost evaluations, while researchers of disaster risk analyses should not lose sight on “persistent” disasters as described in this study, which affect more human capital and may imply degradation in production factor inputs.
The data that support the findings of this study are available from the corresponding author on request.
DG and YX designed the study and YX carried it out. JM constructed the multiregional input–output table for China, 2012. YL and YS provided the requested dataset. YX prepared the paper with contributions from all co-authors.
The authors declare that they have no conflict of interest.
This article is part of the special issue “In-depth study of air pollution sources and processes within Beijing and its surrounding region (APHH-Beijing) (ACP/AMT inter-journal SI)”. It is not associated with a conference.
This work was supported by the National Key R&D Program of China (2016YFA0602604), the National Natural Science Foundation of China (41629501, 71873059, and 71533005), the Chinese Academy of Engineering (2017-ZD-15-07), the UK Natural Environment Research Council (NE/N00714X/1 and NE/P019900/1), and the Economic and Social Research Council (ES/L016028/1).Edited by: Pingqing Fu Reviewed by: two anonymous referees