Tung Oil

China׳s 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines

C I(HINESE tung oil, also called 'Chinese wood in commerce, is obtained from seeds of trees of the Euphoribiaceae family. In China there are two principal species, namely, Aleurites fordii Hemsl. and A. montana Wils. Trees belonging to the former species grow abundantly in most parts of China. They are a fast growing tree, about twenty feet in height, with a strong trunk and many flat-topped branches covered with foliage and flowers. They usually come into their first flowering and bear little fruits during the third to fifth year after planting, yield a variable production thereafter according to age, and decline rapidly after twenty-five years. In the middle of September each year, fruits are collected from a fully matured tree; in each fruit there are three to five seeds with a hard shell and oleaginous kernel. The seed when dried contains 36 per cent oil, but the practical yield of oil by the primitive method used in rural areas amounts to only 24 to 31 per cent. This method consists of the following steps: seeds are roasted, ground into powder, the powder is put in a hollowed-out log, and pressure is applied with wedges on a movable partition. In this way the tung oil is expressed. A second pressing of the residue is generally made, from which a low-grade oil is obtained which is usually mixed with that of the first extraction. Almost all Chinese tung oils are so obtained, although the modern hydraulic press is also utilized in a few tung oil producing centers. No complaints of poor quality have been heard since the Chinese government set up the Bureau for Testing and Inspection of Commercial Commodities to prevent tung oil from being adulterated with other lower priced oils. This oil is a thickand at low temperatures, semi-solid substance, pale yellow to dark brown in color, possessing a somewhat unpleasant taste and strong characteristic odor. Since it consists chiefly of the glycerides of elaesteric, lenolic, and oleic acids, it possesses even more strongly pronounced drying power than linseed oil, forms a skin more rapidly than the latter, and therefore constitutes one of the vital materials for manufacturing industries. In China the oil is used in preserving, polishing, and water-proofing wood, in making water-proof cloth, umbrella paper, and water-proofing bamboo netting, and as an ingredient of lacquer and paint. It is also used as lamp oil, and in the treatment of boils, ulcers, swellings, and burns. The cake obtained as a by-product in the preparation of tung oil is used as fertilizer or in the manufacture of lampblack. In foreign countries tung oil is widely used as a substitute for linseed oil, mainly in the manufacture of paint, varnish, enamel, oilcloth, linoleum, printing ink, electric insulating material, etc. It is also regarded as an effective insecticide capable of destroying insects which infest the roots of certain crops. From the standpoint of world production China has much to boast of, claiming more than 95 per cent. She is the world's largest tung oil exporter. The volume of exportation showed an

In this research, under the guidance of scientific and available principles, an evaluation index system for the green total factor productivity of new infrastructure construction and the sports industry was constructed. The evaluation was conducted using Stata16, the DEA-Solver PRO13 software, and GIS technology using the entropy weight method, super-efficiency SBM model, and other methods. The results indicated the following: First, the overall level of new infrastructure in China is low (mean 0.255), being slightly higher than that of information infrastructure (mean 0.230), innovation infrastructure (mean 0.190), and convergence infrastructure (mean 0.555). The level of information infrastructure, especially innovation infrastructure, in eastern China is much higher than that in central and western China, especially western China. Second, the sports industry in most Chinese provinces is effective in terms of technology and scale and is in a constant stage of scale return, while the remaining provinces are in a rising stage of scale return. The mixed efficiency in the sports industry of eastern China is at a higher level than its scale efficiency and pure technical efficiency, while the mixed efficiency levels in the sports industries of central and western China are greater than those of the pure technical efficiency but less than the scale efficiency. Meanwhile, the level of mixed efficiency in the sports industry of northeast China is far lower than that of its pure technology and scale efficiency. There is still room for improvement in the discharge of pollutants and labor practices in the sports industry, especially in the sports service industry. Third, the impact of the new infrastructure and its three subsystems on the sports industry is significantly positive at the 1% level. By region, the marginal effect of information infrastructure in eastern China is the largest (2.469), while the effect of innovation infrastructure in central China (5.113), western China (4.866), and northeast China (3.251) is the largest.

Spatiotemporal patterns and spatial risk factors for visceral leishmaniasis from 2007 to 2017 in Western and Central China: A modelling analysis

Fertility decline in most countries of sub-Saharan Africa has thus far started later and proceeded more slowly than in countries in Asia and Latin America and the Caribbean undergoing the transition in fertility since the 1950s from high levels to near-replacement or even below-replacement levels (Bongaarts and Casterline 2013). Yet there is considerable variation among countries in sub-Saharan Africa: in the duration and magnitude of fertility decline, whether stalls in fertility decline have occurred, shifts in the timing of births, and even the economic and population subgroups that have led declines in family size (Caldwell, Orubuloye, and Caldwell 1992; Bongaarts and Casterline 2013; Cleland, Onuoha, and Timæus 1994; Cohen 1998; Ezeh, Mberu, and Emina 2009; Garenne 2008; Kirk and Pillet 1998; Rossier, Corker, and Schoumaker 2015; Timæus and Moultrie 2008). With current fertility estimated at 5.1 births per woman in the region and 19 countries in sub-Saharan Africa at or above that level and another 21 countries with at least four births per woman on average (United Nations 2015a), the pathways that future fertility takes will significantly determine population growth and age structure shifts not only in the region, but increasingly for the world. Sub-Saharan Africa is projected to grow from 840 million people in 2010 to nearly 1.4 billion in 2030 (United Nations 2015a). Above-replacement fertility is projected to account for 61 percent of this population increase from 2010 to 2030 compared to 4 percent from mortality reduction, 37 percent from a young age structure in 2010 (population momentum), and a small negative contribution from migration (United Nations 2015b). These projections draw on the United Nations medium variant and do not take into account the uncertainty around current and future fertility levels, uncertainty that only increases the farther the projection period extends. For high-fertility countries in sub-Saharan Africa, the wide uncertainty around where fertility is headed can result in substantial differences in population projections (Ezeh, Mberu, and Emina 2009; Fuchs and Goujon 2014; Gerland et al. 2014). Beyond population numbers alone, the uncertainty about fertility decline also bears on policy-relevant questions such as the degree to which the region may realize a demographic dividend (e.g., how fast the shift will occur toward a higher ratio of working-age population to non-working-age population and the consequent effects on economic growth) (Bloom et al. 2013) or the extent to which greenhouse gas emissions might be reduced by slowing population growth (O'Neill et al. 2010). Earlier reviews of the fertility transitions in sub-Saharan Africa through the 1980s and 1990s showed fertility declines underway in most countries and particularly rapid declines in several countries in Eastern Africa (Kenya, Rwanda, and Zimbabwe) and Southern Africa (Botswana and South Africa) (Cleland, Onuoha, and Timæus 1994; Cohen 1998; Kirk and Pillet 1998). Subsequent survey data suggested an apparent slowdown in the pace of fertility decline in more than ten sub-Saharan African countries (Bongaarts 2008). Further analyses of the data indicated far fewer stalls in fertility decline had in fact occurred in the region, with evidence strongly supportive of stalls in Kenya and Rwanda, and the stalls that do occur have been of relatively short duration (Garenne 2011; Machiyama 2010; Schoumaker 2009, 2014). Most countries in sub-Saharan Africa still lack complete and accurate vital registration data on births, so these and other analyses of fertility trends will continue to rely heavily on survey data and require reconciling estimates from different sources (Alkema et al. 2012; United Nations 2015c). Our aim in this chapter is to provide an updated and concise description of the diversity of fertility decline patterns among countries1 in sub-Saharan Africa, drawing on the latest series of fertility estimates that take into account many different data sources and that are harmonized with other demographic components (United Nations 2015d). We focus on the level of fertility prior to the start of fertility decline, the time period of the fertility transition, and the estimated pace of decline. We also explore the implications of different fertility decline patterns for future fertility and population projections in the region. We draw on the distinct patterns of fertility decline among countries worldwide that are advanced in (or have completed) their first fertility transition to construct probabilistic fertility and population projections for sub-Saharan African countries. The illustrative comparisons of projections highlight the demographic impact if future fertility decline in sub-Saharan countries were to accelerate and follow the rapid pace of decline already experienced by a diverse group of countries. The UN Population Division publishes estimates and projections of period total fertility rates in World Population Prospects (WPP) every two years. The estimates of total fertility presented in this chapter are from the 2015 Revision and are for five-year time periods for countries or areas with 90,000 persons or more in 2015 (United Nations 2015a). The most recent data underlying the total fertility estimates from the 2015 Revision for 50 sub-Saharan African countries2 (United Nations 2015c) are from the period 2013–2014 for 14 countries, 2010–2012 for 30 countries, and 2005–2009 for six countries. A common challenge in estimating total fertility over time, especially for countries without accurate or complete vital registration data,3 is that estimates vary across data sources and by the methodology used to derive those estimates. Schoumaker (2014) showed that the underlying data from standardized, high-quality surveys such as the Demographic and Health Surveys vary considerably within and across countries, yielding total fertility estimates from recent fertility data of good quality (e.g., Gabon, Lesotho, Namibia, and Zimbabwe) and of poor quality (e.g., Benin, Burkina Faso, Cameroon, Chad, Ethiopia, Guinea, Madagascar, Mali, Mozambique, Niger, Nigeria, and Uganda). Total fertility estimates based on births in the last three years tend to be under-estimated by 10 percent or more in most of the surveys with poor quality fertility data from retrospective birth histories. Figure 1 illustrates the variation in total fertility estimates based on survey data and estimation methods (direct methods (D) and cohort-completed (C) fertility) for Nigeria for the period 1985 to 2015. The thick lines show the total fertility estimates from the 2010, 2012, and 2015 Revisions of WPP. Given new data from the 2008 DHS and other surveys, total fertility in the 2012 Revision was re-estimated at a higher level than the 2010 Revision beginning in the mid-1980s, resulting in almost half a birth per woman difference in the 2005–2010 period. The 2015 Revision used new data from the 2013 Demographic and Health Survey. The 2013 survey estimates highlight a recurring pattern in which fertility estimates based on a recent reference period are consistently lower than fertility estimates from reconstructed birth histories for the same time point. Looking only at fertility estimates from a three-year reference period, the 2013 DHS shows a decline in total fertility to 5.5 births per woman from a stalling pattern of 5.7 births per woman in the 2003 and 2008 DHS. Yet the absolute differences are large between these three-year reference period estimates and those for the same time point from the reconstructed birth histories: about half a birth difference in the mid-2000s (comparing the 2008 and 2013 survey estimates) and about one birth difference in the early 2000s (comparing the 2003 survey estimate to those from the 2008 and 2013 surveys). Estimates of the total fertility rate for Nigeria 1985–2015 based on various data sources and estimation methods, and WPP estimates from the 2010, 2012, and 2015 Revisions NOTES: DHS = Demographic and Health Survey; MICS = Multiple Indicator Cluster Survey; MIS = Malaria Indicator Survey. (C) refers to cohort completed fertility (i.e., average number of children ever born) for women aged 40–44 and 45–49 at the date of the survey and backdated using their mean age of childbearing. (D) refers to direct fertility estimates based on maternity histories or recent births in the 12 or 24 months preceding the survey. SOURCES: Federal Office of Statistics of Nigeria (1992); National Bureau of Statistics (2008, 2012); National Population Commission (2000, 2002, 2004, 2009, 2012, 2014). WPP fertility estimates consider as many types and sources of empirical estimates as possible, including retrospective birth histories and direct and indirect fertility estimates (Gerland 2014). The 2015 Revision updated all total fertility estimates taking into account new data and the inconsistencies among estimates. Moreover, total fertility estimates are derived to ensure as much internal consistency as possible with all other demographic components and intercensal cohorts enumerated in successive censuses (United Nations 2015d). The advantages of this approach are that the estimates are internally consistent within a country over time and with respect to other related demographic information. A disadvantage is that the estimates can depart from a country's official estimates of fertility. Figure 2 shows the estimated trends in period total fertility for sub-regions of sub-Saharan Africa from 1950 to 2015. It was high (above six births per woman) in all sub-regions in 1950–1955. Fertility remained high in Eastern and Western Africa until the 1980s, after which it began a slow decline to 4.9 births per woman in Eastern Africa and 5.5 in Western Africa in 2010–2015. Fertility in Middle Africa began to decline a decade later and more slowly, reaching 5.8 births per woman in 2010–2015. Southern Africa departed from the overall trends with a decline beginning in the 1950s and dropping below three births per woman in the 2000s. The 2010–2015 estimate of 2.5 births per woman in Southern Africa is about half the total fertility level in Eastern, Middle, and Western Africa. Sub-regional trends in total fertility, sub-Saharan Africa, 1950–2015 SOURCE: United Nations 2015a The sub-regional fertility levels mask diverse levels among countries. Figure 3 shows country-specific total fertility levels in 2010–2015. Among the 16 countries in Western Africa, total fertility ranged from 2.4 in Cabo Verde to 7.6 in Niger. One additional country had a fertility level of more than six births per woman (Mali), six countries had fertility between five and six births per woman (Burkina Faso, Côte d'Ivoire, Gambia, Guinea, Nigeria, and Senegal), and seven had fertility between four and five births per woman. Total fertility levels in countries in Africa, 2010–2015 SOURCE: United Nations 2015a. Total fertility levels ranged even more widely among the 20 countries in Eastern Africa, from 1.5 in Mauritius to 6.6 in Somalia. One additional country in Eastern Africa still had a fertility level above six in 2010–2015 (Burundi) and six countries had fertility between five and six births per woman (Malawi, Mozambique, South Sudan, Uganda, Tanzania, and Zambia). Eight countries had fertility levels between four and five births per woman. The lowest levels of fertility were in Djibouti (3.3 births per woman) and in the small island countries of Mauritius, Réunion, and Seychelles (less than three births per woman). The nine countries of Middle Africa all had fertility levels at or above four children per woman. In Angola, Chad, and DR Congo, fertility levels were six or more births per woman, and in the remaining six countries fertility levels were between four and five births per woman. While fertility in Southern Africa is largely dominated by South Africa's pattern, the range in fertility among the five countries in the sub-region is narrow, from 2.4 births per woman in South Africa to 3.6 in Namibia. Both Botswana and South Africa now have fertility levels below three births per woman. The start of the fertility transition also varies widely across sub-Saharan Africa. We analyze when and at what level of fertility a country experienced a maximum total fertility level before the onset of fertility decline. This maximum is defined as the most recent five-year time period where total fertility is within half a child of the overall maximum fertility in the country over the 1950–2015 estimation period, thus excluding random fluctuations in pre-transition fertility (Alkema et al. 2011). Figure 4 shows the diversity across countries and within sub-regions in the level and time period of the maximum fertility level before the onset of fertility decline, as assessed in the 2015 Revision. By the late 1970s, 29 sub-Saharan countries were on the verge of a fertility decline, increasing to 40 countries by the early 1980s. While the maximum fertility before the onset of fertility decline was reached in all countries in Southern Africa by the late 1970s, the range of experiences was much wider among countries in Eastern Africa (from the early 1950s in Réunion to the late 1990s in Somalia), Middle Africa (from the late 1960s in Angola to the late 1990s in Chad and DR Congo), and Western Africa (from the early 1960s in Cabo Verde to the late 1990s in Niger). Maximum total fertility in the time period before the onset of fertility transition, sub-Saharan African countries by sub-region SOURCE: United Nations 2015a. The maximum fertility before the onset of fertility transition ranged from less than six births per woman in five countries (Central African Republic, Equatorial Guinea, Gabon, Lesotho, and Seychelles) to more than eight births per woman in two countries (Kenya and Rwanda). There was a positive but weak relationship between the maximum fertility level and the timing of when fertility transition commenced (R2 = .04). The transition from the maximum fertility to the current estimated fertility level in 2010–2015 has been slow for most countries in sub-Saharan Africa. We examine both the maximum decline in fertility in a five-year period and the duration of time between the maximum fertility level and when a country achieved a 10 percent decline in total fertility. A 10 percent decline in total fertility is one of several empirical rules that researchers have used to identify when fertility has begun to decline in a sustained manner from a pre-transitional maximum (Bongaarts and Casterline 2013; Coale and Treadway 1986; United Nations 2014), including differences in the time period selected to identify the onset of fertility decline (Casterline 2001) and the magnitude of decline (e.g., 5 percent decline with further conditions for subsequent changes applied, see Bryant 2007). While the different rules all strive to distinguish sustained decline from random fluctuations in fertility levels, each rule will have different repercussions for interpretations about the timing of onset of fertility decline, duration and pace of decline, and correlations with levels of development and other indicators. In seven countries the first steps of fertility decline took place over a long time period. In Angola, Gambia, and Uganda, a 10 percent decline from the maximum fertility level took 40 years; and in Lesotho, Mozambique, Niger, and Tanzania, it took 30 to 35 years (Appendix Table 1).4 In other countries the fertility decline never gained speed. Overall declines from the country-specific maximum fertility level to the level in 2010–2015 were very slow in nine countries (Angola, Congo, Gambia, Mali, Mozambique, Niger, Nigeria, Uganda, and Tanzania), where average fertility declines were 0.2 children per woman or less, a pace at which it would take at least 25 years to realize a decline of one birth per woman (Appendix Table 1). During the fertility transition, some countries experience an acceleration of fertility decline that, based on historical experience of past transitions in Latin America and the Caribbean and Asia, might reach a decline of more than one birth per woman in a five-year period (United Nations 2015a). In most countries in sub-Saharan Africa, however, the maximum fertility decline is much smaller. The largest declines of more than one birth per five-year period were registered in the early-transition countries in Eastern Africa before 2000 (Djibouti, Mauritius, Mayotte, Réunion, Rwanda, Seychelles, and Zimbabwe). For some countries the maximum fertility decline is projected to be reached in the future: one country in Eastern Africa (Mozambique), four in Middle Africa (Angola, DR Congo, Equatorial Guinea, and Sao Tome and Principe), and five in Western Africa (Gambia, Guinea, Mali, Niger, and Nigeria). Figure 5 shows that countries reaching the maximum fertility decline later tend to reach it at lower levels (the correlation across the 50 countries is R2 = .45). Maximum five-year decline in total fertility since the onset of fertility transition, actual and projected, sub-Saharan African countries by sub-region SOURCE: United Nations 2015a. The pace of fertility decline is illustrated in Figure 6 for Ethiopia and Nigeria, the two most populous countries in sub-Saharan Africa. Estimated levels of total fertility from 1950–1955 to 2010–2015 are shown with a fitted model of the fertility transition. Both countries reached maximum fertility levels before the onset of fertility decline between the late 1970s and early 1980s, and both reached a 10 percent decline from this maximum in the early 2000s. While Ethiopia reached the peak pace of fertility decline in 2005–2010 (a decline of nearly one birth in the five-year period), Nigeria's fertility decline has been consistently slow, with a low peak pace of decline. The maximum fertility decline per five-year period in Nigeria is projected to be reached in the future (a decline of 0.3 births in 2020–2025). The difference in the pace results in current total fertility of 4.6 births per woman in Ethiopia and 5.7 in Nigeria. The 80 percent prediction intervals (dashed lines in Figure 6) around the probabilistic projections of total fertility indicate the magnitude of the different fertility changes that could reasonably happen. For example, by 2045–2050 there is a one in ten chance that total fertility in Nigeria could be as low as 2.6 or as high as 4.4 (Figure 6 and Appendix Table 2). Observed decline in total fertility from 1950–1955 to 2010–2015, predicted decline, and time period of maximum TFR and maximum decline, Ethiopia and Nigeria SOURCES: United Nations 2015a and computations by authors. Given the slow pace of fertility decline in most sub-Saharan African countries thus far and the uncertainty around the pace of future declines, what might be the impact on future fertility levels and the growth in total population if fertility declines in the region followed an accelerated pace that has already been experienced by countries that have completed or are nearing completion of the fertility transition? To answer this question, we generated probabilistic fertility scenarios for 2015–2100 using a modeling approach described in Alkema et al. (2011) and implemented in a publicly accessible software package BayesTFR (Ševčíková, Alkema, and Raftery 2011). The standard United Nations 2015 probabilistic results pool the experience of all countries having similar fertility levels and trends (the baseline scenario). An alternative probabilistic scenario was created using only the fertility transition experiences of 21 countries5 that shared a similar pattern of accelerated decline in fertility (the accelerated scenario): specifically, a slow pace at the start of the transition that sharply rises to a peak pace of decline before steadily tapering off after total fertility has reached about four births per woman. These 21 countries, which include—among countries with more than 10 million inhabitants in 2015—Bangladesh, China, El Salvador, Morocco, Peru, South Africa, Sri Lanka, Thailand, Turkey and Uzbekistan, represent disparate institutional, social, economic, and cultural contexts and yet experienced a similar pattern of a relatively abrupt acceleration of fertility decline. The objective of this exercise is to examine the impact on population size of fertility decline that is more rapid than currently projected, and to illustrate the implications of such a hypothetical scenario. While we do not theorize or analyze the factors that produced these rapid fertility declines, the fact that a similar pattern of fertility decline took place within such a diverse group of countries raises the possibility that a similar decline could occur within a region that also reflects quite distinctive and diverse contexts. Each fertility scenario is used to simulate 10,000 probabilistic population projections for 2015–2100 under identical conditions (i.e., using the same mortality and migration assumptions) that show the population growth trajectories if sub-Saharan African countries were to follow a specific fertility decline pattern.6 The implications of this specific fertility decline pattern for future fertility trends are shown in Figure 7 for Ethiopia and Nigeria. The accelerated fertility decline scenario leads to much more rapid declines for these countries. By 2045–2050, median projected total fertility in Ethiopia would decline from 2.3 (baseline) to 2.0 (accelerated decline) and in Nigeria from 3.6 (baseline) to 2.6 (accelerated decline) (see Appendix Table 2). Probabilistic fertility projections (median and 80 percent prediction intervals) for two scenarios: baseline and accelerated fertility decline, Ethiopia and Nigeria SOURCES: United Nations 2015a and computations by authors. The accelerated fertility scenario has greater implications for projected fertility in Nigeria, where the recent pace of fertility decline has been much slower than in Ethiopia. The projected medians of the accelerated scenario are similar to the lower bound of the baseline scenario for parts of the projections, suggesting that Ethiopia and Nigeria would need to experience a rapid fertility decline, similar to the rapid declines experienced by the group of 21 countries, in order to realize the lower-bound fertility level of the baseline scenario. The assumption that Ethiopia and Nigeria would follow the fast fertility decline experiences of these 21 countries leads to substantially lower population projections compared with the baseline scenario (Figure 8 and Appendix Table 3). If Ethiopia experienced an accelerated fertility decline, the projected population grows from 99 million in 2015 to 182 million by 2100 (80 percent prediction interval of 93 million to 324 million) compared with the higher median projection of 234 million under the baseline scenario. If Nigeria adopted the accelerated fertility decline pattern, the projected population grows from 182 million in 2015 to a median of 466 million by 2100 (80 percent prediction interval of 282 million to 777 million) rather than the faster projected growth to 737 million under the baseline scenario. Probabilistic population projections (median and 80 percent prediction intervals) for two scenarios: baseline and accelerated fertility decline, Ethiopia, Nigeria, and sub-Saharan Africa SOURCES: United Nations 2015a and computations by authors. The counterfactual of sub-Saharan African countries following an accelerated fertility decline, as experienced by the group of 21 countries, results in a projected increase in total population in the region from 962 million in 2015 to 3.2 billion by the end of the century (with 80 percent probability of being between 2.8 and 3.7 billion). The total population projection for the baseline UN 2015 scenario would lead to a median projection of about 4 billion, or a difference of 770 million people with an 80 percent prediction interval mostly above the upper bound of the accelerated fertility decline scenario. We presented here an updated description of fertility decline in sub-Saharan Africa and have explored the implications for fertility and population projections if sub-Saharan African countries follow a pattern of accelerated fertility decline. Because estimates of fertility in the region rely almost entirely on survey and census data, there is still uncertainty about fertility change over time. Our analyses of trends over time were based on a new historical time series of total fertility estimates for the period 1950 to 2015 from the 2015 Revision of World Population Prospects. Fertility has remained persistently high in Eastern, Middle, and Western Africa (4.9 births per woman or higher as of 2010–2015) and declined rapidly in Southern Africa, which currently has about half the total fertility level of other sub-regions births per woman). Southern Africa is dominated by South Africa, TFR of 2.4 is the lowest in the Yet there is wide variation in total fertility across countries, even within from below fertility births per woman) in Mauritius to 7.6 births per woman in Niger. The fertility level before the onset of decline was high in all countries in sub-Saharan Africa, from less than six births per woman in five countries to more than eight births per woman in two countries. By the late 1970s, 29 countries were on the verge of fertility decline, increasing to 40 countries by the early 1980s. The transition from these maximum levels of fertility prior to the onset of fertility decline to current levels has been slow for most countries, including seven in which it took 30 years or more to a 10 percent decline in total fertility. While countries in Asia and in Latin America and the Caribbean experienced an acceleration in fertility decline of more than one birth per five-year time period, this level of acceleration has been experienced thus far by only seven countries in Eastern Africa before the The pace of fertility decline in sub-Saharan Africa will a large in the magnitude of future growth in We showed through an illustrative scenario that an acceleration in the pace of fertility decline to that already experienced by 21 countries South Africa) would the growth in future population in the region from a projected billion by to billion and total population size from a projected 4 billion people by the end of the century to 3.2 While these 21 countries represent a wide range of institutional, social, economic, and cultural a similar fast decline from fertility levels of six or more children per woman in the past to less than two or three in recent years. this accelerated pace of fertility decline to countries at of transition illustrates the substantial impact on future population growth of other possible patterns of fertility decline in sub-Saharan Africa. of this are assumption that the fertility estimates from the World Population Prospects are and the fact that uncertainty around the estimates of period total fertility in recent is not into By scenarios on the distinct fertility decline patterns that have been experienced thus far by countries that have completed or are advanced in their fertility we new patterns of fertility decline in the Because most countries in Middle Africa and Western Africa are still in the early of fertility transition, an sub-Saharan African pattern of fertility decline may still and Timæus The is not for the or of by the authors. than be to the for the

The difference and convergence of total factor productivity of inter-provincial water resources in China based on three- stage DEA-Malmquist index model

Does the digital economy reduce air pollution in China? A perspective from industrial agglomeration

The field of migration studies is increasingly widening its scope and research by addressing the complex and converging interactions between migration, internal displacement, livelihoods, and poverty. Within this space, internal migration and displacement are assuming recognition as more contextual and layered processes for vulnerable populations in the global south. A growing body of evidence indicates that regions in East and Southeast Africa and South Asia continue to witness a surge in internal and intra-regional movements of people who are termed as climate or environmental refugees. Resultantly, they are likely to move, are on the move, as also those who are unable to move and are left behind, and those who stay. The paper examines the close and often blurring linkages between environmental migrants, (climate) refugees, aspirational and economic migrants. Engaging with the question of why people move, we observe that the ‘climate-migration’ nexus is intricate and far from a facile, reductionist narrative, as is the ‘migration as adaptation and agency’ nexus –issues that are intertwined when perceiving climate migration in Africa and Asia.

Using tung oil as the raw material, a new bio-based prepolymer was successfully synthesized by reacting with acrylic-modified rosin (β-acryloyl nutrient ethyl) ester (ARA)/acrylic-2-hydroxyethyl ester (HEA) followed by the use of the above composite material as the matrix and then reacting with the active diluent (2-HEMA, TPGDA) and the photoinitiator TPO and Irgacure1173 to successfully synthesize a new type of bio-based prepolymer-acrylate-epoxy tung oil polypolymer (AETP). The tung oil monomer before and after the epoxy formation was compared by proton NMR spectroscopy, and the chemical structure of AETP was analyzed by Fourier transform spectroscopy. Tung oil has an acid value of 1.5 mg KOH per g, an epoxy value of 5.38%, an iodine value of 11.28 g/100 g, and a refractive index of n25 = 1.475. Composite-based 3D printing resins (like AETP) were cured using digital light treatment, while some samples were also post-treated via ultraviolet (UV) light treatment. The AETP-based 3D printing resin has excellent thermal and mechanical properties, and the viscosity of its system is 313 mPa s; exposure time 4.5 s; the tensile strength, flexural strength and flexural modulus were 62 MPA, 63.84 MPa and 916.708 MPa, respectively; Shore hardness was 80 HD and shrinkage was 4.00%. The good performance of the AETP-based 3D printing resin is attributed to the rigidity of their tightly crosslinked structure. This study pioneered a method for producing photoactive acrylates (e.g., tung oil-based acrylate oligomer resins) from renewable, low-cost biomass for light-curing 3D printing.

Regional heterogeneity of China's energy efficiency in “new normal”: A meta-frontier Super-SBM analysis

Polypropylene (PP) fibres and yarns are sometimes called polyolefin (PO) when, along with PP, reference is made to polyethylene (PE), which comprise a minor part of them or fibres based on ethylene and propylene polymers. Currently, in terms of global production volume, PP fibres occupy the second place among all types of chemical fibres, lagging much behind polyester fibres [1], but taking part in realization of a growing raw material potential (Fig. 1) that will maintain the trend toward rising demand and capacities of the original polymer for a foreseeable period [2]. In Europe, to which PO fibres owe their origin, they comprise, according to European Textile Polyolefin Association data, 45.2 % of the chemical fibres produced [3]. On the average, about 12 % of the PP granulate produced is processed into fibres and yarns in the world, including Russia, although higher figures are often cited in some sources, which depends on specific regions, the range of products manufactured there, technological direction, level of development of the processing branches, and other factors. In 2011, all polyolefins (PO) of most widespread types of fibre products (Table 1), primarily film yarns, registered growth (+3 %) [4]. Outputs of complex yarns, which include BCF carpet tow, monofilament, and spunbond, attained a high level in Western Europe, China, USA, and Africa, and India, Canada, and South America recorded the highest growth rate. Large PP staple fibre production plants are concentrated in Western Europe, USA, and China (the growth rate relative to 2010 was the highest in the latter). In addition to spunbond, the most actively growing textile products from PP are film yarns (raffia) where China, Western Europe, Africa, Turkey, USA, South America, and Eastern Europe remain the leaders in terms of production volume, and South America, Japan, and Africa, in terms of growth rate in 2011 (+9, +7, and +5 %, respectively). From an analysis of Table 1 we find that a marked decline in production of all types of PO fibres in USA and Mexico. In USA, for example, complex yarn production capacity shrunk by 6 % over the past five years [5], and production of other yarns remained practically at the same level (Table 2). Let us describe in some more detail the situation in the European market since it significantly affects the Russian market. The share of PP fibres in the demand for all types of textile raw material in Europe is 38.9 % and in chemical fibre output is 44.3 % [3]. In this continent, 24.1 % of PP granulate is used for producing fibrous materials (Fig. 2). In the above-noted figures the share of polyethylene is minuscule. New PP spunbond production capacities are being actively introduced in Europe, especially in its eastern part, primarily in Russia where the annual growth rate is the highest in the world, and in Turkey (Table 3). In the context of the European Commission Directive 94/62/EC, PP spunbond must substitute PE film for making packages because it has the best performance properties (high strength, attractive exterior look, and wide-ranging decorative possibilities), reduces deleterious effect on the environment, the packages are capable of being used in a variety of ways and of undergoing secondary processing. Production of PP spunbond has been rising steadily: since 2010 the number of applications from different continents rose to 23 for installation of high-efficiency plants with total capacities of 400,000 tons/yr, which will meet in the period 2012-2015 the rising demand for this relatively light and strong material [6]. Appearance of new plants in South Africa, China, South America, and Indonesia is contributing to the growth of PP spunbond output in these regions. Among the known methods of nonwoven materials (spunlaid, airlaid, carding, spunlace, etc.) production, spunbond is the most popular right now. It accounts for almost one-half of the global production of all types of nonwoven materials (NM), more than 36 % of which is manufactured from PP (Fig. 3). In output volume, the second place belongs to NM produced by carding technique (cloth making from staple fibre) with an output of 3.4M tons in 2012 [4]. Needle-

Journal of the Society of Chemical IndustryVolume 55, Issue 16 p. 311-311 Article Research association of British paint, colour, and varnish manufacturers First published: 17 April 1936 https://doi.org/10.1002/jctb.5000551606AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume55, Issue1617 April 1936Pages 311-311 RelatedInformation

The development of the social public resource digital sharing system (SPRDSS) has been accelerated with the evolution of digital information and communication technologies (ICTs). This paper analyzes the dissipative structure features and formation process of SPRDSS in China. Combined with the Brusselator model and its transformation, this paper empirically analyzes the dissipative structure of SPRDSS using panel data collected from 30 Chinese provinces (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2015 to 2019. The results show that the SPRDSS in China has pre-conditions to form a dissipative structure. At present, the SPRDSSs in most Chinese provinces have not yet formed the dissipative structure, but they are gradually evolving into it. The global orderliness of the sharing system is greater in eastern China than in central China and greater in central China than in western China. The potential for improving global orderliness is greater in western China than in central China and is greater in central China than in eastern China. Therefore, proper policies and measures should be adopted to accelerate the construction of SPRDSS based on the evolution of dissipative structure and to promote the sustainable development of the digital sharing economy.

Using Total Ozone Mapping Spectrometer (TOMS) version‐7 level‐2 clear‐sky (reflectivity ≤ 20%) ozone measurements corrected for aerosol effects and sea‐glint errors, we derived Lower Tropospheric Ozone (LTO) west and east of the Andes, the Mexican and Rocky Mountains, the mountains in Africa and the Arabian Peninsula, New Guinea, and the Himalayan Mountains. The derived results agree reasonably well with the seasonality of LTO from ozonesonde observations at Boulder, Cristobal, Fiji, Java, and Tahiti. The LTO seasonality found in the biomass burning seasons characterized by the ATSR World Fire Atlas west and east of the Andes (23°S–2°N), east of the Mexican Mountains (15°–23°N), South Sudan (6°–14°N), South Africa (30°–28°S), and west of New Guinea is consistent with the influence of biomass burning on the formation of tropospheric ozone in these regions. The significant El Niño influence on LTO west of New Guinea is evident throughout several El Niño cycles. The spring maximum in ozone west of the Mexican Mountains, in western China, and west of the Andes (32°–23°S) is consistent with a stratospheric intrusion source. East of the Mexican Mountains (23°–30°N), both west and east of the Rocky Mountains, in north Sudan and Iraq, and in western China, high concentrations of ozone are found in these continental and coastal regions which are affected by anthropogenic sources. The maximum ozone in these regions usually occurs in the summer due to photochemical ozone production. A summer LTO minimum occurs in coastal regions west of the Andes and west of Mexico, due to ozone destruction in low NOx and high H2O marine environment. A summer minimum also occurs in south Sudan in the rainy season. The LTO in the northern tropics of South America (4°–10°N), Africa (1°S–2°N), and east of New Guinea (7°–3°S) experiences little seasonal variation.

Infant and young child feeding (IYCF) has not been documented in central and western China, where anaemia is prevalent. To support policy advocacy, we assessed IYCF and anaemia there using standardized methods. A community-based, cross-sectional survey of 2244 children aged 6-23 months in 26 counties of 12 provinces. Analysis of associations between haemoglobin concentration (HC), IYCF indicators and other variables using crude and multivariate techniques. Only 41.6% of those surveyed consumed a minimum acceptable diet. Fewer still-breastfeeding than non-breastfeeding children consumed the recommended minimum dietary diversity (51.7 versus 71.9%; P<0.001), meal frequency (57.7% v. 81.5%; P<0.001) or iron-rich food (63.3% v. 78.9%; P<0.001). Anaemia (51.3% overall) fell with age but was significantly associated with male sex, extreme poverty, minority ethnicity, breastfeeding and higher altitude. Dietary diversity, iron intake, growth monitoring and being left behind by out-migrating parents were protective against anaemia. A structural equation model demonstrated associations between IYCF, HC and other variables. Meal frequency, iron intake and altitude were directly and positively associated with HC; dietary diversity was indirectly associated. Health service uptake was not associated. Continued breastfeeding was directly associated with poor IYCF and indirectly with reduced HC, as were having a sibling and poor maternal education. Infant and young child anaemia is highly prevalent and IYCF is poor in rural central and western China. Continued breastfeeding and certain other variables indicate risk of poor IYCF and anaemia. Major policy commitment to reducing iron deficiency and improving IYCF is needed for China's rural poor.