extfieldsofstudy: Economics Medicine

s2fieldsofstudy:
Economics
Political Science

text:
Escaping from poverty trap: a choice between government transfer payments and public services

Background Anti-poverty has always been an important issue to be settled. What policies should be selected to help individuals escaping from the poverty trap: by directly offering transfer payments or indirectly providing public services? This paper is among the first to explore the effects of public anti-poverty programs system in China. Methods We Using unbalanced panel data of China Health and Nutrition Survey (CHNS) from 1989 to 2009, we demonstrate how the individual poverty status is determined through a four-staged simultaneous model. We choose the 3SLS (Three Staged Linear Squared) methodology to do the estimation. Results GTPs (Government Transfer Payments) don’t have positive effects on poverty reductions. The results demonstrate that GTPs increasing by 10% makes private transfer payments decrease by 3.9%. Meanwhile, GTPs increasing by 10% makes the household income decreased by 27.1%. However, public services (such as medical insurance, health services, hygiene protection etc.) have significantly positive impacts on poverty reduction. Public services share a part of living cost of the poor, and are conducive for people to gain higher household income. Conclusions GTPs given by governments are not effective in reducing the poverty, as a result of “crowd-out effect” and “inductive effect”. However, public services are suggested to be adopted by governments to help the poor out of the poverty trap.

Background
China has seen a fast economic growth over the past thirty years. However, there are still a number of people suffering from poverty. Since the mid-1980s, Chinese government has implemented a series of public policies to fight against poverty, including heavy investment on antipoverty programs. According to China Statistical Bureau, the poverty rate in China has fallen from 30.7 to 1.7%. While the absolute poverty population, defined as people who are below the national standard poverty line, drops from 250 million to 14.78 million from 1978 to 2007.
However, the anti-poverty work becomes more and more difficult to step forward. For instance, the poverty reduction rate was roughly about 1.5% each year from 1978 to 1999, but the rate has been held up around 0.26% for the following eight years. Moreover, some new problems, known as "temporary poverty" and "non poverty-back-to-poverty" raise up. They somehow challenged the sustainability of the on-going anti-poverty work. For example, a large number of people are migrating from rural areas into cities nowadays, and they may become the potential poor as a result of higher living cost in cities.
The fiscal budget is stringent and fiscal resources are limited. Therefore, it is of key importance to explore what are the most effective policies to help the low-income people out of poverty trap. Generally speaking, there are three main tools to reduce the poverty rate. The first is known as government transfer payments(GTPs). By using this policy, the government sets the standard to distinguish target groups and directly offer cash funds to them. There are kinds of government transfer payments in China. They include but are not limited to public grants to the disabilities, 1 cash transfers to people whose spouse(or parents) dies(die) as a result of working, subsidies to people whose total earnings is below the national minimum living cost 2 etc. Overall, GTPs means direct cash funds offered by governments to the poor who need financial help.
However, governments can also help the poor by providing primary public services, such as education, health services, sanitation etc. As it doesn't mean government offering cash to the specific group directly, public services can be seen as an indirect way for governments to help people out of poverty. Basically, public services do not target any special group, i.e., residents have equal access to public services once they are provided. The cost of the services (or goods) are mainly undertaken by governments instead of individuals. Although primary public services may not reach the best qualities, they essentially meet the basic demand of people. All of the residents including the poor are eligible to have free access to utilize these services or they spend just a few user fees in gaining the usage.
The last financial resource the poverty could obtain is known as private transfer payments(PTPs). They could be offered by their employers, or relatives, or friends. This kind of transfer payments can be in the form of cash or in-kind benefits.
According to the previous literature, government transfer payments are often justified by their presumed effects on poverty reduction, as GTPs seem to increase the income of low-income group. However, the empirical evidence are ambiguous and inconclusive. By using the country-level data, a strand of literature find that public transfer payments can significantly reduce poverty [1][2][3]. Some relevant studies also highlight the importance of GTPs in anti-poverty system for the low-income residents in rural China, who are characterized as the "absolute poverty" [4]. Du & Park [5] believe that the governmental transfer system in urban China can effectively target the poor and help them out of the poverty trap. Wu and Ramesh [6] empirically study poverty-reduction effects of the Minimum Living Standard Assistance Program in China. They find that governments directly giving money to the poor is proved to be an effective tool for poverty reduction [6]. Similar findings are also provided by other studies [7,8].
However, some studies come to opposite conclusions. They find GTPs don't have positive effects on reducing poverty [9][10][11]. More studies offer explanations for the failure of GTPs. Basically, there are two major problems associated with this policy. One is known as "funds misappropriation". In reality, GTPs are initially allocated by the central government, but implemented by prefecture governments(or cities for brevity). In many cases, without strict supervisions, city governments have strong inclinations to divert these funds to support local economic growth rather than helping the poor. Therefore, the lower-level governments, such as counties and townships, usually find themselves lack of money to offer subsidies to the poor. The other one is called "targeting errors". In other words, there are probabilities that high-income level people are mistakenly regarded as the poor who are in need of financial help [12,13]. Xia et al. [14] apply with Chinese Household Income Project(CHIP) data set and find that anti-poverty policies have little effect on reducing urban poverty in China. Avram [15] examines in a comparative setting the role social assistance plays in reducing income poverty in eight Central and East European countries. Wang & van Vliet [16] explores the developments of social assistance and minimum income benefits across 14 Western European countries, 12 Central and Eastern European countries and 7 non-European countries. They find that the effects of governmental direct assistance on poverty reduction depend on institutional environment conditionally.
From another perspective, some literature prove that public services could reduce the poverty by increasing people's earning capacity [17]. Some studies find that public expenditures on education and medical services have positive impacts on reducing poverty [18][19][20][21]. Strauss & Thomas [22], Quisumbing [23] discover that health insurance plays an important role in helping people out of poverty. Other public services, such as water supply, sewage disposal services as well as electric power facilities, are proved to effectively improve the living standard of low-income people [10,24]. However, there are exceptions. Castro-Leal et al. [25] examines the impact of public spending on education and health care in several African countries. They find these programs are not pro-poor [25]. Wagstaff et al. [26] estimate the pro-poorness index of government health expenditure across 69 countries. They also find government health expenditures are pro-rich [26]. Similar evidences are provided by some other studies [27][28][29].
Besides GTPs and public services, private transfer payments(PTPs) can also help the poverty. As other individuals directly offer cash or in-kind benefits, it enhances the total income of the poor. However, some literature find out the crowd-out effect between GTPs and PTPs. Increasing the amount of GTPs(PTPs) would be likely to reduce the amount of the other one [30,31].
As far as we have discussed, the impact of GTPs (public services as well) has not come to consensus. The most important reason lies in the endogeneity concerns. For example, whether GTPs result from the poverty status or whether poverty status results from GTPs is unclear. Both of these cause-and-effects relationships are possible, suggesting that GTPs and the individual poverty status are likely to be jointly endogenously determined. Moreover, since there may exist the crowd-out effect between GTPs and PTPs, PTPs may also be determined simultaneously as soon as GTPs approach the equilibrium point.
To account for this potential simultaneity, as well as examine interrelationships among GTPs, PTPs and public services, we estimate the empirical relationship between GTPs and poverty status using the simultaneous equation models(SEM). At the same time, we introduce four determination equations into SEM models: the household income, GTP, PTP and poverty status. And we use the three-staged least squares method to do the estimation. By applying with this methodology, we could not only overcome endogeneity concerns, but also demonstrate interrelationships among these three anti-poverty tools.
In this paper, we aim to do several tests with panel data from China Health and Nutrition Survey (CHNS). Firstly, how do GTPs, PTPs and public services affect the poverty status respectively, after accounting for endogeneity concerns. Secondly, how would GTPs, PTPs and public services affect each other. Thirdly, we check for heterogeneous effects.
This paper contributes to the extant literature as follows: Firstly, to the best of our knowledge, this is the first paper to study the effect of governmental transfer payments on poverty reductions in China. By solving the endogeneity concerns, it justifies the "crowd-out effect" as well as the "inductive effect" of GTPs. Secondly, SEM is applied to reveal interrelationships among GTPs, PTPs and public services. We overcome endogeneity concerns raised by using a single equation. Finally, our paper enriches the literature by providing empirical evidence at household-level.
The rest of paper is organized as follows: Section 2 introduces the identification framework and data, empirical results are presented in section3, followed by section 4 to further discuss the mechanisms and do robustness checks, and Section 5 is to conclude.

Identification framework
As aforementioned, SEM suits rightly to estimate the impact of GTPs on the poverty status, as well as explore interrelationships among three anti-poverty policies. Our estimation model can be expressed as follows:  [2]. The whole estimation system includes four equations. Equation (1) specifies the household income, as a function of four exogenous variables: demographic characteristics, householder's working type, householder's educational level and public services. Equation (2) specifies the government transfer payments as a function of the household income and two exogenous variables, including demographic characteristics and policy-dependent characteristics. Equation (3)specifies private transfer payments as a function of two endogenous variables(household income and GTPs), and three exogenous variables(demographic characteristics, working types and family assets). Equation (4) specifies the poverty status as a function of three endogenous variables(household income, GTPs, PTPs), and two exogenous variables(demographic characteristics and public services). Overall, household income, GTPs, PTPs and the poverty status are endogenous variables and determined simultaneously. While public services, as well as other controlling variables, are exogenous variables and predetermined.
Methodologically, as four variables are jointly determined and error terms of these four models may be correlated, OLS is not appropriate to estimate the model. Usually, two-staged least squares(2SLS) and three-staged least squares (3SLS) are mostly used. By adopting 2SLS method, it means that we regress household income on all the exogenous variables in Equation (1) (for an example), and then we estimate the fitted value of household income. In the next step, we use the fitted value of household income as an IV variable into Equation (2), et cetera. This could yield consistent estimates of parameters because the fitted value of the first stage is uncorrelated with the error term in the next stage regression. However, heteroscedasticity may still exist among our estimators of four different models. While by using 3SLS, we eliminate the heteroscedasticity concerns by using GLS estimation after the 2SLS method.
We also control for year fixed effects and county fixed effect in each equation. By satisfying both the rank condition and the order condition, SEM system can be only specified. We have several robust checks by changing our systematic models to various specifications and including different variables. However, this doesn't change our results much.

Data and variables Data sources
The data we use come from "China Health and Nutrition Survey" (CHNS), implemented collaboratively by the Carolina Population Center at the University of North Carolina at Chapel Hill and the National Institute for Nutrition and Food Safety at the Chinese Center. This survey aims to provide data for study of disease control and prevention. And CHNS database is widely used for studies of poverty, health and income inequality [32][33][34][35][36].
During 1989-2009 year period, eight waves of household surveys are held annually. The database draws the sample restricted roughly to 4400 households with 19000 individuals from nine provinces that are broadly representative of China's rich regional variation. The provinces include Liaoning, Heilongjiang, Jiangsu, Shandong, Henan, Hubei, Hunan, Guangxi and Guizhou. Liaoning and Heilongjiang is heavy industry provinces in the northeast; Jiangsu and Shandong are dynamic high growth provinces in China's east coastal regions; Henan, Hubei and Hunan are less developed provinces in the middle of China; Guangxi and Guizhou are much less developed ones in the west of China.
There are also several caveats to mention. Basically, the questioned households are roughly the same for each wave of survey. However, new households enter or extant households exit our sample each wave. About 6.9% of the sample, on average, is refreshed randomly each wave of the survey. Thus, the panel data is not balanced. In addition, community questionnaire is also implemented at commune-level, from which we are able to obtain data of public services.
Two data sets, including both "household survey" and the "adult survey", are drawn from CHNS. For the former one, it provides detailed information about the entire household. They include but are not limited to total household income, income sources, family background and other characteristics at household-level. However, until now, we are not able to obtain any information for householders. To control for characteristics at householder-level, we match "household survey" with the "adult survey" through year and ID code. 3 "Adult survey" data set provides details of adults who are over 18 years old, such as demographic status, occupations, etc. It also asks every respondent if he(she) is the householder of his(her) family. Therefore, we have information of householders.
Sample distribution and variable statistics are presented in Table 1. As shown in Table 1, for each survey year, we have about 4,000 households. The sample is proportionally distributed among nine provinces, each province has about 11% of total households. Four waves of the survey, all of which are after 2000, account for 55% of total sample. This ensures representativeness of our sample to reflect present circumstances. Households from urban areas are significantly less than that from rural areas. Rural households are twice as big as urban sample. Multi-children families constitute almost over 7% of total observations in each province, whereas "onechild" family is about 3% comparatively.

The variables
We have 32,147 household-year observations. Here we give details of our key variables in the analysis: Income According to Equation (1), three types of income sources have to be estimated.
Firstly, household income (Y) is total earnings gained at the household level divided by the number of family members. The earnings include salaries paid by employers for the employed family members, retirement payments for the retired ones, self-gains for self-employed family members. 4 Household income also include transfer payments from both governments and other private individuals. We sum up all these earnings to get total household income.
GTPs include all cash funds from the government that the household are able to get. The indicator is also in per capita form. If transfers from the government are inkind benefits, the survey also asks "how much money of these in-kind benefits value". Finally, we sum up GTPs of all family members within the household.
Similarly, PTPs are transfers either from employers, relatives or friends. PTPs are also measured at household-level, in the form of per capita.
Public services From questionnaires, we have information about three kinds of public services provisions. They are medical insurance, clean water provision and residential hygiene protection. Equivalently, we have four dummy variables to measure public services provisions. The first dummy variable measures whether the householder has national primary medical insurance. The second dummy variable indicates whether the household have an access to clean water provision. 5 The third dummy variable denotes whether the household have toilets inside their house, this indicator reflects public hygiene protection services. Similarly, the last dummy variable is also about hygiene conditions protected by municipal environment entities. It measures whether there are any excrement around the living place.
Poverty status Poverty status is measured by a set of dummies:(1) The first poverty status dummy estimates whether the household's income is below the average income level of their county. The household's income is calculated as total household's income divided by the number of family members. If the household's income is below average county level, it equals to 1, otherwise it equals to 0. (2) The second poverty status dummy measures whether household's income is less than county income-level at 25 quartile. The dummy equals to 1 for below and 0 for above.(3) Finally, we define the poverty status dummy according to family properties. If the household do not have a color television, the dummy variable equals to 1, otherwise 0.
Householder characteristics We have a set of controlling variables of householder's characteristics. They include gender(1 for male, 0 for female), age, marital status(1 for yes, 0 for 0), ethnicity(1 for minority, 0 for Han ethnicity), migrant status, 6 educational level, employment status, occupation and a dummy variable measuring whether the householder has got a second job.
Households characteristics Another set of controlling variables are used to measure characteristics at household-level. They include identity registration (1 for urban residents, 0 for rural residents), political connection(a dummy variable equals to 1 if any member from the household works in the government); a dummy variable equals to 1 if the household is identified as the "Five-Guarantee". 7 And the dummy variable indicates whether the household is a "one-child" family or not (1 for yes, 0 for no).
Family properties Five kinds of family assets have been inferred in the survey. The respondent is asked whether to have a car, an air-conditioner, a camera, a washing machine or a fridge, respectively. The equivalent dummy variable equals to 1 for yes and 0 for no.
Several caveats to mention. On one hand, we choose price in 2006 as the benchmark to do price deflation. This enables us to compare present prices to previous ones. On the other hand, all variables are in per capita form. For example, poverty dummies are all constructed based on household income per capita. It ensures households of different sizes to be comparable.  Table 2 gives detailed definitions and summary statistics of all the variables used in the analysis.

Results
The baseline results Table 3 represents the baseline results of SEM regressions. In model I, the poverty status is measured as whether the household's income is below the average income level of their county. In model II, the poverty status is measured as whether household's income is less than county income-level at 25 quartile. Column 1 to column 4 show results corresponding to Equation (1)- (4). Similarly, column 5 to column 8 also show results from Equation (1)- (4).

Panel A in
As shown in column 2 as well as column 3, higher income-level household is associated with more transfer payments from both governments and other individuals. The coefficients of household income variable in GTP determination model and PTP determination model are both significantly positive. The results imply that GTPs and PTPs are essentially pro-rich. We are able to get similar results by estimating Model II.
The impacts of different anti-poverty policies on poverty reduction are presented in column4 and 8. We find that higher household income is associated with   Notes: To identify the mechanism, we introduce the interactive term between the poverty status and GTPs into PTP determination specification. And we also introduce he interactive term between the poverty status and GTPs into household-income determination specification. We only demonstrate results of the interactive term in Panel B. Other variables are not reported. Z values are reported in parentheses. a Significantly at 10% level. b Significantly at 5% level.
c Significantly at 1% level lower possibilities to become the poverty. Government transfer payments significantly increase the probabilities to become the poor, while private transfer payments are statistically negative related to the poverty status. The baseline results are partially consistent with previous literature. As suggested by [14], direct government transfers to the poor seems to have insignificant impact on poverty reduction in China. Then we explain why GTP do not protect people from being the poverty. To examine the underlying mechanism, we firstly test the crowded-out effect between GTPs and PTPs. We introduce the interactive term between the poverty status and GTPs into Equation (3). We find that the interactive term is significantly negative. Economically, GTPs received by the poverty increase by 10%, PTPs decrease by 3.875%. The crowd-out effect is more pronounced among absolute low-income group, as the magnitude of the interactive term in Model II is larger than that of Model I.
We secondly examine the inductive effect of GTP on household income. Similarly, we introduce the interaction between GTPs and the poverty status into Equation (1). We find that the interactive term is also significantly negative. GTPs increase by 10%, the household income decrease by 2.705%. According to [37], GTPs make people more dependent on the external help from governments. They become more "lazy" instead of working hard, especially the lowskilled ones. We get similar results as we estimate the inductive effect in Model II.
We find that public services reduce the probabilities of being the poverty significantly, as can be seen from column 4 and column8. Meanwhile, we find that public services may reduce poverty through two mechanisms. Firstly, By providing some basic services, the government helps the poverty through sharing more living cost which are supposed to be taken by the poverty themselves. Secondly, it helps the poor gain higher household income and thus reduce the poverty indirectly.
The results for controlling variables are roughly similar to the previous literature. If the householder has got a job, then the entire household are more likely to have higher income level but receive less transfer payments from the government. Migrants do not obtain higher level of GTPs than native residents, however, they receive more PTPs actually. Householder's age affects household's income and GTPs in a non-linear way. On one hand, the household get more income and GTPs as the householder is becoming older. On the other hand, the magnitude of the age effect decreases as time goes on. The householder who has got a second job is associated with higher income level, however, entire family would not receive more PTPs from either employers or relatives. The household have the highest income level if the householder works in private companies. While the household gain less income if the householder works in agricultural sectors, governments or state-owned firms. However, the household obtain more PTPs when the householder works in agricultural sectors, governments and state-owned companies. Finally, educational background significantly increases household income.
People from urban areas gain higher household income and PTPs. Households in rural areas obtain more GTPs. Households that are partially related to governments have less income but more GTPs and PTPs. Surprisingly, "one-child" families have less transfers from governments. Households with considerate family properties obtain more PTPs. Overall, the results suggest that PTP is pro-rich, and the lowincome group also benefit little from GTPs.

GTP, public services and poverty status conversion
In this section, we examine whether GTPs or public services have significant impact on poverty status conversion. To test the relationship, we divide the sample into two groups. One group contains households who have seen no change in their poverty status, i.e., households remain the non-poverty or the poverty during the sample period. The other group contains households who have been changed at least once in terms of poverty status, i.e., from the nonpoverty status to the poverty status, or from the poverty status to the non-poverty status. In the poverty status conversion group, we require each household must at least have two observations from different wave of survey. The poverty status is also defined as we do in Table 3. The results are presented in Table 4.
The results show that household income as well as PTPs have negative effect on poverty status conversion. In other words, they prevent the non-poverty people from being the poverty again. While the GTPs have a significant impact on promoting the non-poverty into the poverty. Public services significantly reduce the incidence of poverty status conversion. By examining mechanisms, the crowd-out effect and the inductive effect still hold. Transfers from governments make the poverty receive less PTPs and become more dependent on external assistance. Moreover, these two effects are more pronounced among the relatively poor group(by using the poverty_ 25% indicator). Other controlling variables remain largely unchanged compared to Table 3.

Heterogeneity test
In this section, we have a heterogeneity test. In China, situations in urban areas and rural areas are quite  Note: We only report the results for household-income determination specification and the poverty-status determination specification in Table 4. a Significantly at 10% level. b Significantly at 5% level. c Significantly at 1% level different. For instance, very few rural residents have formal jobs. Neither do they have as many financial resources as urban residents do. There are also essential differences in the way of the GTP allocation process for these two groups. Being lack of strict management, GTPs in rural areas could be more likely to be misused. We divide the sample into two groups. One group contains households in urban areas while the other contains households from rural areas. We re-run SEM models and the results are shown in Table 5.
For urban residents, no matter they are migrants or natives, GTPs have no significant impact on reducing the poverty incidence. For rural residents, GTPs prevent the poverty from being out of poverty trap. However, almost all public services significantly reduce the poverty incidence. By checking the mechanisms, the "crowd-out " effect as well as the "inductive" effect holds whatever sub-sample we estimate. These two effects, however, are more pronounced among relatively lower-income level group in rural areas.

The impact on the absolute poverty
The above analysis measures the poverty from relative perspective. But considerate people are labeled as the absolute poverty in China, as they are totally disabled or have been caught in serious illness [10]. In order to estimate the impact of GTPs (public services) on the absolute poverty status, we focus on the absolute poverty sub-group. We only keep households who make livings mainly on transfer payments(including GTPs and PTPs) or government subsidies. When household receive more transfers or subsidies than any other kinds of income sources, the household is regarded as the absolute poverty. As transfers are allocated on the basis of more strict regulation, we further use the sub-sample who are mainly dependent on transfers. Results are shown in Table 6.
The results show some interesting findings. The GTPs have insignificant impact on reducing the absolute poverty incidence. But they do not encourage the absolute poverty incidence neither. While public services still significantly reduce the absolute poverty rate. The "crowd-out" effect and "inductive" effect of the GTPs on the absolute poverty are estimated. Surprisingly, the interactive term between GTPs and PTPs is significantly positive, implying that GTPs lead to more PTPs. This may be the demonstration effect generated by government transfers. When the government increases its public transfers to help the absolute poor, it also encourage other social members to help them together. Whereas, GTPs still has negative impact on enhancing the household income, considering about the inductive effect.

Robustness checks
In this section, we have two robustness checks: On one hand, we change the measurement of poverty status. On the other hand, we change model specifications.

Change the measurement of poverty status
In first robustness check, we use three other measures of poverty status. The first poverty status measures whether household's income is less than county median income-level(1 for yes, 0 for no). The second measures whether household's income is below county incomelevel at 10 quartile(1 for yes, 0 for no). The final one measures whether the household have the most basic family property, i.e., TV. The results are presented in Table 7.
As shown in Table 7, our results remain largely unchanged. Government transfer payments have significantly negative effect on poverty protection. GTPs crowd out private transfer payments received by the household and make the poverty more dependent on the government. Whereas, public services have significantly positive effect on poverty reductions. Public services help the poverty gain higher household income and share living cost which are supposed to be taken by the poor themselves.

Adjustment of model specifications
In baseline regressions, both GTPs and PTPs are not affected by poverty status. However, it may be the case that the poverty are able to gain more (or less) GTPs and PTPs. Therefore, in the second robustness check, we introduce poverty status variable into different subspecifications: household income determination equation, GTP determination equation and PTP determination equation. We re-estimate the simultaneity equation models and results are reported in Table 8.
As shown in Table 8, GTPs fail to protect the poverty in China. While public services reduce poverty incidence significantly. The crowd out effect and inductive effect still hold.

Policy selection: from GTP to public services
We have empirically examined the impact of both GTP and public services on poverty reductions. Meanwhile, we test the interrelationships among GTP, PTP and public services. GTPs fail to protect the poor while public services are conducive to help the poverty out of poverty trap. Results are robust after considering about the heterogeneity and different model specifications.
GTPs mainly aim to help specific poor groups. These cash funds are initially given by the central government, while it is ultimately managed by local governments.  Significantly at 5% level. c Significantly at 1% level  Significantly at 5% level. c Significantly at 1% level  Usually, GTPs have to be transferred through multiple layers of governments before they reach to the poverty. Each level of government does have strong incentives to misuse these funds for other economic purposes, such as infrastructure. It is also well known that the management of GTP is not with strict supervision. High income-level people, in some cases, are usually mistaken as the target group. Although GTP do have positive impact on reducing the absolute poverty, they crowd out the amount of PTP and make people more dependent on external sources. However, public services essentially reduce poverty incidence. On one hand, they directly share the living cost which are supposed to be taken by the poverty. On the other hand, they help the poverty enhance earning abilities to gain higher household income. Moreover, targeting errors can be well prevented, as public services are equally provided to all the residents. In reality, the poor benefit from basic public services as much as the rich do. In summary, it is more optional for governments to choose public services as main anti-poverty policy.

Conclusion
In this paper, we empirically test the effects of different anti-poverty policies on poverty reduction. We use eight waves of CHNS survey data, and apply with SEM to do the estimation. We find that GTPs are pro-rich, i.e., people with higher income obtain more government transfer payments. Meanwhile, GTPs crowd out PTPs that the poverty can receive and make them more dependent on external help. These effect are more pronounced among relatively low-income level group. However, public services significantly reduce poverty incidence and help to increase the poverty's earning abilities. The effects are robust after the inclusion of other control variables as well as estimating with different specifications.
Anti-poverty has always been an concerning issue. To fight against poverty problems, many anti-poverty tools have been provided. As some of the policies cost a substantial amount of fiscal budget, whether they remain effective in reducing the poverty incidence matters a lot to governments. According to this study, governments directly offering cash to the poverty is not effective in reducing poverty incidence, whereas, public services are suggested to be adopted by governments to help the poor out of poverty trap.

Endnotes
1 Cash transfers to people who have been totally or partially lost their abilities to work. The amount of grants to people who are eligible for this scheme depends on the local budget. 2 The minimum living cost standard is made by the national government. The amount of subsidy standard differs from city to city. Actually, it depends on the fiscal budget of each city. However, the bottom line is to make sure applicants at least are able to catch up with the national minimum living cost. 3 Each household has a code and each respondent has been required to document his(her) family code. 4 If the respondent is a businessman, his household income is his earnings from business involvements. If he is a peasant, household income is gains from agriculture activities. 5 Actually, in China, most urban residents have access to use clean water resources provided by municipal drinking-water companies. While many rural residents still have to dig water from underground by themselves. 6 In the questionnaire, there are two ways to identify the migrant status. Item A16 asks the respondent "whether you have always been living here. A21 asks the respondent"what is your home province". We compare the respondent's host province with his home province, and we are able to identify whether he is a migrant or not. By adopting this method, we reduce the possibilities when the respondent responses at will. 7 If the household is identified as the "Five-Guarantee", it means the commune will take care of this family in terms of food consumption, clothing, medical care, housing and burial expenses.


The ETS in China and Europe: dynamics, policy options and global sustainability perspectives

The Emission Trading Schemes of China and Europe show that China’s envisaged national ETS could bring a major contribution in the international approach against global warming; new perspectives on the use of composite sustainability indicators are also highlighted. China’s regional pilot schemes will converge to a (more) uniform price of emission allowances. As China is a major economic and political actor in the world economy, China’s progress with ETS is important. At the same time, China’s progress in the field of green international competitiveness – standing for a positive revealed comparative advantage in environmentally friendly goods – in the period 2000–2015 is considerable and the improved positioning of China in the EIIW-vita sustainability indicator shows considerable technological dynamics in Asia. The European ETS is working, but it suffers from the rather low price of emission allowances. The long-term time horizon of 2050 in the EU climate policy is rather ambitious and it is unclear whether or not a consistent G20 approach can be achieved – with the EU, China, Japan and the US cooperating amongst each other. There is a lack of a specialized climate stabilization institution in the world economy, the traditional anchoring of climate policy in the UN weakens the practical pressure for efficient cooperation since the UN is very heterogeneous in terms of per capita income and GHG emissions per unit of GDP; G20 might be an institution that is suitable for effective policy cooperation. More initiatives in the field of recycling could be useful.

Introduction
Regional Emission Trading Systems (ETS) have been popular in the US and Canada, in the EU (the EU ETS covers all 28 member countries plus Norway, Iceland and Liechtenstein) and in China which will switch from a phase of separate emission trading markets in seven pilot regionswhich has operated from 2013 to 2017 -to a nationwide system in 2018. The biggest ETS actor in the US is the state of California which has some cooperation with Quebec in Canadahowever, the prospects that the US will come on board with a national ETS system of its own in the medium term are almost zero, particularly since the Trump administration has even announced that the US will withdraw from the Paris Agreement. From the perspective of Economics, global ETS could be an ideal method to achieve a reduction of greenhouse gas emissions in an efficient way (for an overview of ETS, both in force and planned, around the world, see Fig. 10 in appendix). The alternative to ETS for internalizing negative external effects could be national Pigou taxes imposed on those activities that generate greenhouse gas (GHG) emissions. In any case, the emission certificate price as well as a Pigou tax are supposed to create incentives for reducing emission-intensive production and for encouraging emission-reducing innovations and the switching from fossil fuels to renewable energies.
For China, the link between these new incentives for reducing GHG emissions and enhancing innovation could be particularly important as China's policy has obviously increasingly emphasized the role of innovation dynamics for future growth and thus appropriate new incentives in the field of climate policy are required.
Regarding a Pigou tax, the general problem is that in the case of cross-border effects one cannot easily impose adequate Pigou tax rates on the relevant source country; the challenge in the case of global warming is that climate protection is a truly global public good. From this perspective, a global ETS could indeed be quite usefulprovided that the certificate trading price is not subject to very high levels of volatility which, for example, could be caused by broader financial market instabilities. During the Transatlantic Banking Crisis it could be observed that many financial market prices showed a high degree of volatility and this also applied to certificate prices for CO 2 trading; this creates the problem that firms and investors, respectively, will find investment decisions with regard to energy-saving technologies to be rather difficult. While it is possible for firms to implement hedging strategies, the optimal investment is rather costly to derive in an environment of high volatility. Countries which are not relying on ETS but are eager to reduce GHG emissions will face higher marginal GHG emission abatement costs than those with certificate trading. Those countries that apply ETS face lower marginal costs in reducing GHG emissions while climate stabilization could generate economic benefits (Stern 2006).
With ETS the speed of adjustment depends partly on competition and hence it is important to maintain a high intensity of competition which helps in the adoption of best practice in green technology modernization. Subsequently, we will take a closer look at the EU and China while also raising the question of whether or not ETS in both the EU and China could be sufficient to have a decisive impact on the world economy, i.e. to contribute to the stabilization of the climate.
The share of renewable energy is rising in many countries due to feed-in tariffs and innovation-driven reductions of renewable energy prices over time. It appears to be important that both policymakers as well as investors increasingly focus their attention on compact composite indicators which are compatible with the OECD's requirements for this type of indicator: one example of such a composite indicator is the EIIW-vita Global Sustainability Indicator (Welfens et al. 2016) which includes the share of renewable energy, the "true savings ratio" -according to the World Bank conceptand the revealed comparative advantage for environmentally-friendly products so that the strength of the relative green export position (RCA in environmentally-friendly goods; based on the OECD classification) is consideredthe latter aspect reflects green innovativeness in the export sector. The EIIW-vita indicator can be downloaded for 140 countries (since 2000) from www.eiiw.eu. China has improved its position in the composite EIIW-vita Global Sustainability Indicator considerably between 2000 and 2015 (+68 rank positions; rank 12 in 2015); with Germany having a lead position in 2015 (see Fig. 1). The ten countries shown in the top section are those that have been the leading ten countries in the field of international competitiveness in environmentally-friendly goods in 2015as measured by the sectoral Revealed Comparative Advantage (RCA: if the sectoral export-import ratio is better than the aggregate export-import ratio, there is a positive specialization in environmentallyfriendly products). Leading countries specialized in environmentally-friendly exports are not only Germany and China but also Italy, Japan, Mexico, Austria, Belgium, Hungary, the United Kingdom and France. The bottom ten countries in the field of specialization in environmentally-friendly products were Luxembourg, the Philippines, Canada, Indonesia, Australia, Spain, Russia, Singapore, Ireland and India in 2015. One may argue that Canada, Indonesia, Australia and Russia are major exporters of natural resources and thus have no strong incentive to specialize in environmentally-friendly products (and importing a relatively large share of environmentally-friendly products could help to achieve a strong sustainability position despite the given export specializationan issue not further explored here).
In terms of the RCA in environmentally-friendly goods, China's improvement is even more remarkable as it shows a changing technological specialization (see Fig. 2). This suggests that China's potential to improve the economic situation in hot spot regions could also be favorable in the long run if adequate framework conditions for the economy are set and appropriate policy measures as well as useful international cooperation (for example, Sino-EU projects) are implemented.
The incentives for investment in countries with a strong positioning in sustainability as measured by the above indicators are, of course, quite important. ETS prices in OECD countries are one element to be considered here, but over time ETS prices have reduced in relative terms (compared to the GDP deflator). There are, however, new perspectives in the EU where the European Commission intends to reduce the allocation of emission permits over time thereby increasing demand and raising prices; at the same time, China's adoption of ETS is a strong push for more global ETS trade. Financial market dynamics could contribute to rather unstable certificate prices under certain conditions. Figure 3 shows the EU certificate price for carbon allowances in Euro per tonne of CO 2 as well as the oil price and the gold price. Basically, one should expect that a rising oil price goes along with a falling demand for oil (and gas) so that the demand for certificates should be positively correlated with the demand for GHG certificates -and here one would have to control for supply-side changes as they occurif, for example, the European Commission would intervene in the market and buy certificates. The graph is supposed to give a first picture of the relationship between the three prices and no econometric analysis is made here (on the empirical link between gold price and oil price see Beckmann and Czudaj 2013). With regard to the gold price, one may consider this asset price to be part of broader financial market volatility. One may argue in any case that strong financial market volatility could have spillover effects into certificate markets. Financial market activities -if adequately regulated -do not necessarily contribute to instability in the financial sector. On the contrary, the combination of adequate competition policy and banking (de-)regulation could contribute to efficiency gains and positive growth effects as has been discussed in part of the literature.

ETS in China
The People's Republic of China has actively participated in the global carbon market since 2005 through the Clean Development Mechanism (CDM) under the United Nations Framework Convention on Climate Change (UNFCCC). The CDM was the world's first global carbon market, allowing developing countries who had ratified the Kyoto Protocol to develop and transfer emission reductions from domestic low-carbon projects to industrialized countries who counted these emissions towards their compliance with targets under the Kyoto Protocol. As a result, China now has several years of As can be seen in Fig. 4, the schemes involve two big cities (Beijing and Shanghai), the industrial municipalities of Tianjin and Chongqing, the provinces of Guangdong and Hubei, and the special economic area of Shenzhen. Fujian province became the eighth pilot market in December 2016. In 2017, conditions in China are conducive to the establishment of a national carbon trading market which, once completed, will be the largest carbon emissions trading market in the world. During the first phase, China's carbon market will cover the emission-intensive enterprises in the eight major industrial sectors: petrochemical, chemical, building materials, steel, nonferrous metals, paper, electricity and aviation. Between 2013 and 2015, the NDRC developed interim administrative ETS measures, as well as sector-specific monitoring and reporting guidelines. Building on this, current work is focused on developing the National ETS Legislation, as well as rules around emissions reporting, verification entities, allocation and offsetting. These regulations are under consultation and are expected to come into force in the first half of 2017. The allowance allocation is also expected to be completed by then.
In 2016, the accumulated trading volume in all the pilot markets accounted for 68.6 MtCO2, with a total value of CNY 1.1 billion (EUR 153 million) (see Figs. 5 and 6). Shenzhen ranked the first for the market value with CNY 298 million, Guangdong saw the largest share of the trading volume (23.4 MtCO 2 ). On the other Fig. 4 Locations of the eight pilot ETS markets in China hand, considering a timeframe from 2014 until now, Hubei ranks the first for both market value (more than CNY 761 million, 32.6% of total) and market volume (37.69 Mt., 36% of total). Figure 7 shows developments in the allowance prices of the EU ETS and the seven Chinese pilot markets from July 2013 to May 2017. In comparing the Chinese pilot markets and the EU ETS, it can be seen that the markets in Beijing and Shenzhen had the closest allowance price to that of the European market.
With the introduction of a nationwide ETS system in China, one can expect that CO 2 emissions in China will continue to drop. Assuming that such emissions in China would approach the level of CO 2 emissions over a decade or so, an analysis of the CO 2 emission figures for the EU in 2013 would indicate that China could potentially reduce emissions by 70% by 2028. To illustrate the impact of such a reduction on the global scale, it would equate to a global reduction of CO 2 in 2013 terms of circa 20% (see Table 3; Fig. 11 in appendix which are based on data from the World Bank (2017)). However, in Europe the ETS is only one aspect of emission reductions, naturally one must also consider technological progress, other innovations and policy initiatives. Therefore, this hypothesis is intended to be illustrative of the effect that ETS, amongst other developments, could have even if implemented by certain large actors, for example, the EU, China and US.

ETS in the EU
Climate policy is a key field of action at the European level where international cooperation and a supranational approach, within the framework of the EU, is appropriate. Indeed, the EU has seen more climate policies enacted and implemented than any of other region in the world (Delbeke and Vis 2016). European countries, both collectively and individually, have been at the forefront of international attempts to combat climate change via the United Nations Framework Convention on Climate Change (UNFCCC) and well-known sponsored agreements such as the Kyoto Protocol, the Copenhagen Accord and the Paris Agreement. It is estimated that in 2013, the EU28 was responsible for 7.87% of global greenhouse gases emissions. This places the EU28 in third place as an emitter of GHG, behind China on 23.67% and the United States on 12.88%including land-use change and forestry (World Resources Institute 2017). According to the WRI data, the European Union has steadily reduced its emissions of GHG since 2006, which led to the EU over-achieving in terms of its GHG reduction target under the first period of the Kyoto Protocol which ran from 2008 to 2012.
The European Union has undertaken a number of EU-level initiatives aimed at countering climate change such as the 2020 climate and energy package, agreed at a European Council meeting in 2007 and enacted in legislation in 2009, the 2030 climate and energy framework, and the 2050 low-carbon economy project. These policy initiatives share certain headline targets which should guide the EU towards becoming a "low-carbon economy" by 2050. These targets can be seen in Table 1. These targets represent EU-wide goals. In order to achieve these targets in a fair and sustainable way, the burden is shared by the member states according to the Effort Sharing Decision, under which wealthier EU member states (determined based on Gross Domestic Product (GDP) per capita relative to the EU average GDP per capita) should reduce more GHG emissions, while some of the poorer member states, primarily the most recent EU accession states in Eastern European, are allowed to increase emissionswith emissions partly seen as a necessity to facilitate the economic catching up of these countries. The burden of cutting emissions is thus shared to a different extent by different member states, but the burden is also shared by different sectors as can be seen in Fig. 8, with the largest cuts in emissions to come from those sectors covered by the European ETS which covers almost half of the EU's GHG emissions (in particular, the power and industry sectors). It is worth noting that this burden sharing model is also applied with regard to the renewable energy targets, where national targets vary considerably taking each country's relative starting position into account. While the EU is on target to meet its 2020 targets, more efforts should be initiated to meet the targets for 2030 and beyond.
These targets are set and monitored at the EU-level and the EU also enacts legislation designed to facilitate achievement of the targets outlined such as the Renewable Energy Directive and the Energy Efficiency Directive. The EU also supports innovation and funding for Research and Development (R&D) in renewable energy and other green technologies. However, most of the decisions and actions needed to meet national targets of member state need to be taken and implemented by national policymakers.
The key to the EU achieving its climate policy targets is the EU Emissions Trading System. The ETS was established in 2005 as the world's first carbon trading market, and it remains the global leader as over 75% of international carbon trading takes place on the ETS (European Commission 2017). The trading market covers all 28 member states of the European Union, along with European Economic Area members Norway, Liechtenstein and Iceland. The ETS operates a 'cap and trade' system, according to which a cap or upper limit can be placed on the total amount of GHG (namely, Carbon Dioxide (CO 2 ), Nitrous Oxide (N 2 O) and Perfluorocarbons (PFCs)) emitted by power plants and manufacturing plants covered under the system. The total number of plants which come under the ETS scheme reaches 11,000 in Europe. This cap can then be reduced easily and cost-effectively over time reducing the supply of allowances and thus increasing the incentive for firms to reduce emissions without the need to purchase allowances. Below the cap, firms can receive free allowances via primary auctions. Firms can buy additional credits or sell unused allowances as required on a secondary marketthe ETS. After the year end, firms must be able to submit enough emissions allowances to cover their emissions for the year. Unused allowances can be kept to be used in future years or traded on the market. If the firm is found to have emitted more GHG than they have allowances to cover, heavy fines will be imposed. By establishing a robust price through open trading, the intention is to create and support incentives for firms to invest in renewable energies and low-carbon technologies. Some member states have argued that the surplus of allowances on the market is preventing a robust price from emerging and thus undermining the aforementioned incentives (see Fig. 9). In April 2016, the European Union signed the Paris Agreement which later entered into force on 4 November 2016. Following President Trump's announcement that the US would withdraw from the Paris Accord, the EU and China jointly agreed to cooperate in terms of GHG reductions, supporting renewable energies and carbon trading.
Subsequently, we briefly look at some individual EU member states and Switzerland (which operates its own emissions trading market). Here, one sees some future developments in relation to the international ETS cooperation and also some of the most important energy-related developments in EU member states, most notably a move away from nuclear energy and fossil fuels and towards low-carbon sources of renewable energy.

Switzerland
As an alpine state with a crucial climate-dependent winter tourism sector, Switzerland faces serious challenges in relation to climate change. However, this non-EU member state which is not part of the EU ETS also has significant opportunities to exploit renewable energy sources, particularly hydro-power. The country is currently is a net importer of energy sources, particularly oil which contributes to GHG emissions. The most important piece of environmental legislation in Switzerland is the so-called CO 2 Act which has targeted reducing GHG emissions within Switzerland. From 2007, the Swiss Federal Council has focused on four key areas: energy efficiency, renewable energies, the replacement of older power plants and the construction of new plants (including nuclear power plants) and an external energy policy. However, following the Fukushima disaster in 2011, the Federal Council decided to re-examine the Swiss energy policy. The result of this review was Switzerland's plan, titled Energy Strategy 2050. Under this new strategy, the existing nuclear reactors in Switzerland are to be allowed to continue to produce energy until the end of their technically safe operating lives at which point the reactors shall be decommissioned and will not be replaced with new nuclear plants (Federal Office of Energy 2017). Concurrently, a new focus will be on developing renewable sources of energy such as hydroelectric, wind, solar and biomass. The Energy Strategy 2050 was to be implemented in a new Energy Law in 2016, however the matter was put to a referendum on May 21, 2017. In that referendum, the Swiss public voted by 58.2% to 41.8% in favor of the proposed gradual phase-out of nuclear power and the support of renewable energy sources (Federal  Chancellery 2017). The Swiss government has announced intentions to cut GHG emissions by 20% of 1990 levels by 2020, and by 50% of 1990 levels by 2030. A major element of meeting these targets is Switzerland's own emissions trading market which was introduced in 2008. Following five years of negotiations, it was announced in 2016 that the Swiss ETS will be linked to the EU ETS allowing a certain measure of cooperation (Federal Office of the Environment 2016).

United Kingdom
The United Kingdom operated a voluntary Emissions Trading Scheme between 2002 and 2006 with just 34 participants. This scheme operated as a quasi-pilot for scheme for the EU ETS (DEFRA 2006). In the United Kingdom, the Committee on Climate Change (CCC) was established in accordance with the Climate Change Act of 2008. The CCC is responsible for detailed analysis and advising the government on carbon budgets, emission targets and other measures designed to support energy efficiency and renewable energy sources. The advice of the CCC informs the government's own policies and strategy and is reflected in legislation. Consecutive carbon budgets have set out the UK's emissions cutting targets: 34% reduction of GHG emissions against 1990 levels by 2020, 50% by 2025, and 80% by 2050 (CCC 2017). The long-term 2050 target was made legally binding as early as 2008, making the UK the first country to implement such a binding target in domestic legislation. Other focus areas are building efficiency, electric vehicles. However, BREXIT will have as yet unforeseen consequences for the EU ETS. With circa 1000 of the 11,000 participants in the EU ETS located in the UK (DBEIS 2017), should the UK complete the process of a "hard Brexit" these firms may, or indeed may be obliged, to leave the ETS which may result in challenges for the EU ETS in terms of the burden sharing arrangements, allowances and on the demand-side.

Germany
In Germany, the key word associated with climate policy is Energiewende. Broad legislation on the so-called Energiewende or "energy transition" was passed in 2010, before the Fukushima disaster. Under this policy, Germany should transition to being a low-carbon economy with a reliable and sustainable energy supply from renewable sources. Initially, nuclear power was seen as an alternative to fossil fuels and coalpowered plants, however following the Fukushima disaster in 2011, a crucial aspect of the transition was added -the phasing-out of Germany's nuclear reactors. Germany targets a 40% cut in GHG emissions by 2020, and 80-95% cut in emissions by 2050. Significant cuts in primary energy consumption and the share of renewable energy are also part of Germany's plan to meet its energy and climate policy goals. However, trading carbon emission allowances is likely to be strong in Germany as Europe's largest economy.

Sweden
In Sweden, a Cross-Party Committee on Environmental Objectives reporting in 2016 has proposed a climate policy framework possibly the most drastic targets of any country with regard to emission cuts. Under this new framework proposal, Sweden will adopt a long-term climate goal which means that by 2045 Sweden will have no net emissions of greenhouse gases. To meet this target would entail an 85% reduction of GHG emissions in relation to 1990 levels. The remaining emissions will be offset by Swedish investment in and support of projects abroad. From 2045 on, it is envisaged that Sweden will attain a situation of negative net emissions by keeping domestic emissions down and increasing support abroad. To help achieve this long-term goal, the government of Sweden has set-out an emission pathway with intermediate targets, government planning and follow-up measures, frequent reports on progress to the Swedish parliament (including annual reports) and the establishment of a climate policy council which would examine the implemented policy on an ongoing basis and assess whether it is compatible with the national climate objectives (Government of Sweden 2016). The proposals also support actions at an EU-level to strengthen the EU ETS system, as the low price for carbon is seen as not representing a sufficient inducement to encourage firms to limit emissions. As a result of its ambitious targets and efforts to reinforce EU measures, Sweden topped Carbon Market Watch's EU Climate Leader Board, ahead of Germany and France, with 67 points from a possible 100 indicating full compatibility with the Paris Agreement (Carbon Market Watch 2017). In January 2017, it was announced by the Swedish Prime Minister that Sweden would enact the climate change law with effect from 1 January 2018 (Darby 2017).

France
France's low-carbon strategy stems from the Energy Transition for Green Growth Act 2015 which was enacted prior to the COP21 in Paris. This national strategy seeks to help France achieve the targets set for cutting emissions which have been agreed at an EU-level. Thus France aims at a 40% reduction (relative to 1990 levels) in GHG emissions by 2030, and has set a target of reducing emissions by 75% by 2050 (French Ministry of the Environment, Sustainable Development, and Energy 2015). Similar to Germany and Switzerland, France is also moving away from nuclear energy but cannot completely end nuclear power production due to its large share. Under the clean energy law passed in 2015, France will cut the share of the power mix produced by nuclear power plants from around 75% in 2015 to 50% by 2025. With a targeted cut in energy consumption, renewable energy sources should account for over 30% of the power mix in the future. Furthermore, a pigovian carbon tax on emission producing fuels such as gas and coal not already covered under the EU ETS, will be increased over time from €14.50 per tonne in 2015, to €56 in 2022 increasing up to €100 per tonne by 2030 (French government 2015).

Policy perspectives
Switching to ETS in China will generate considerable benefits for Asia and the world economy. Emission-intensive production will be relocated from China to Asian partner countries and a higher green innovation intensity in China will help to reduce GHG emissions. The Asian Infrastructure Investment Bank provides opportunities for China to encourage the introduction of ETS in neighboring countries. This in the end could bring a double economic and political dividend for China whose reputation gains in the global green community. The benefits from regional infrastructure projects could be rather large.
If the US does not adopt a nationwide ETS while China and the EU have implemented emission certificate trading, then the pressure on the US will become stronger over time. This could be the first time that the US is facing pressure to follow the EU and China which would be counter to the traditional G20 leadership. The reduction of economic growth in China in the context of a national ETS system is likely to be minimal if one considers the strong improvement of China's revealed comparative advantage in environmental-friendly goods over time as calculated in the form of the EIIW-vita global sustainability indicator (Welfens et al. 2016).
Part of GHG emissions is related to consumption, part is due to the use of machinery for producing goods. Thus the capital intensity k:=K/L (K is the capital stock, L is labor) plays a role for emissions. In an enhanced neoclassical growth model it is obvious that the steady state capital intensity k# (# is steady state) could be above the "golden rule k" which maximizes per capita consumption C/L (C is consumption).
If market dynamics bring about a ratio K/L that is above this level the CO 2 emissions could be reduced while per capita consumption can be raised (Welfens 2017a). This means that governments should be careful to take into account basic insights from growth models and take the golden rule as a useful policy concept.
It may also be emphasized that broad recycling policy initiatives could be quite useful for both OECD countries and the BRICS: the benefits can be well understood in an enhanced ecological Solow growth model (Welfens 2017b) in which part of labor is used for recycling activities R" (parameter φ) which in turn effectively reduce the depreciation rate of capital δ (this is the normal depreciation rate). The new depreciation rate is δ"= (1-R")δ. With a savings function S = (s(1-τ) + s'ττ')Y (0<s<1, s'>0, τ is the income tax rate, τ is the VAT rate) and a rather simple macro production function Y= (M/P) ß' K ß (AL(1-φR")) 1-ß-ß' , one gets a steady state solution for the capital intensity k'# (k':= K/(AL); M is the stock of money, m':= (M/P)/(AL), P the price level; 0<ß<1; 0<ß'<1; knowledge A and labor input L are assumed to grow at rate a and n, respectively: k'# = [(s(1-τ)+ s'τ'τ)m' ß' (1-φR") 1-ß-ß' /((1-R")δ + a + n)] 1/(1-ß) . It is easy to see that the input requirements for recycling -see the term φR"reduces the level of the growth path in the steady state while the improvement effect in the capital depreciation rate (see the term (1-R")δ) is raising the level of the growth path. Therefore one can also raise the question of what is the long-run output per capita maximizing recycling intensityor what is the optimum R". It is obvious that improved recycling is one pillar of a circular national and global economy.
In an open economy there could be international transfer of recycling knowledge which raises the issue of cooperation in recycling innovations of the US, the EU, China and Japan. While this is mainly a question for industrial firms and government policies, respectively, the issue of encouraging households to become more active in recycling and to purchase recycled goods more often is probably more related to education, adequate labeling and green social digital networks.
A key policy issue also concerns the strategic orientation of investment funds and investors, respectively. One could emphasize here the problem of decarbonization and therefore the risk related to investing in natural resources and products that are GHG intensive in the production process. Many investors already have these challenges on their portfolio radar. An equally interesting aspect is whether or not international investors are developing some preference for countries that are leading in the field of sustainabilityfor example, those with a leading position in the EIIW-vita Global Sustainability Indicator. There is still research needed in order to understand whether there is a positive link between, for example, the EIIW-vita global sustainability ranking and the longrun stock market performance of the respective countries. However, there are some solid arguments indeed why one may expect countries with an ambitious environmental regulation to stimulate innovations in general and those in environmentally-friendly products in particular. This should lead to a higher output growth and, with per capita income rising, the demand for a clean environment should intensify in households. Thus the quality of life is improved in this pro-sustainability setting which in turn facilitates attracting the best scientists, managers, skilled workers or students from countries all over the world (with Switzerland or Californiaas a US state -apparently being good examples here). In this respect, it is interesting that China has become one of the leading countries where students from Europe like to study abroad and with air quality further improving in the futurefor example, in the context of growing electro-mobility -China's position could improve even more over time. With sustained output growth, ecological modernization will remain a policy priority in China. What is lacking to some extent is a stronger cooperation between the EU (Eurozone), China, Japan and the US; ranging from macroeconomic policy to green innovation policy. These four countries stand for a group of independent actors: the policy action of each country has a domestic impact as well as an impact on the other three big economies and there will be repercussion effects to be considered from these big partners. An isolated policy strategy thus is doubtful and a cooperation forum such as the G20 could be quite useful as an institutional basis for enhanced cooperation.
China's implementation of ETS will be a big step forward for China and the world economy. ETS trade should become global in the long run. From this perspective the role of G20 countries in ETS should be reinforced (see Table 2 on GDP and CO 2 /GHG shares of G20 countries). Here the future long run income convergence may be expected to facilitate consensus building in this rather heterogeneous group. International cooperation in R&D policy of the EU and China as well as other countries could be quite useful in the long run. EU countries and China might want to consider options for stronger multilateral cooperation in the futureas the Trump administration has emphasized America First, undermining multilateralism (and hence the role of International Organizations) and bilateralism. With the US retreating from global leadership new Sino-EU political cooperation is inconceivable.
The European ETS is working, but it suffers from the rather low price of emission allowances. The long-term time horizon 2050 in the EU climate policy is rather ambitious and it is unclear whether or not a consistent G20 approach can be achieved with the EU, China, Japan and the US cooperating amongst each other. There is a lack of specialized climate stabilization institution in the world economy, the traditional anchoring of climate policy in the UN weakens the practical pressure for efficient cooperation since the UN is very heterogeneous in terms of per capita income and GHG emissions per unit of GDP; G20 might be an institution that is suitable for effective policy cooperation.  Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.