A Spatial Survival Model for Risk Factors of Under-Five Child Mortality in Kenya

Child mortality is high in Sub-Saharan Africa compared to other regions in the world. In Kenya, the risk of mortality is assumed to vary from county to county due to diversity in socio-economic and even climatic factors. Recently, the country was split into 47 different administrative regions called counties, and health care was delegated to those county governments, further aggravating the spatial differences in health care from county to county. The goal of this study is to evaluate the effects of spatial variation in under-five mortality in Kenya. Data from the Kenya Demographic Health Survey (KDHS-2014) consisting the newly introduced counties was used to analyze this risk. Using a spatial Cox Proportional Hazard model, an Intrinsic Conditional Autoregressive Model (ICAR) was fitted to account for the spatial variation among the counties in the country while the Cox model was used to model the risk factors associated with the time to death of a child. Inference regarding the risk factors and the spatial variation was made in a Bayesian setup based on the Markov Chain Monte Carlo (MCMC) technique to provide posterior estimates. The paper indicate the spatial disparities that exist in the country regarding child mortality in Kenya. The specific counties have mortality rates that are county-specific, although neighboring counties have similar hazards for death of a child. Counties in the central Kenya region were shown to have the highest hazard of death, while those from the western region had the lowest hazard of death. Demographic factors such as the sex of the child and sex of the household head, as well as social economic factors, such as the level of education, accounted for the most variation when spatial differences were factored in. The spatial Cox proportional hazard frailty model performed better compared to the non-spatial non-frailty model. These findings can help the country to plan health care interventions at a subnational level and guide social and health policies by ensuring that counties with a higher risk of Under Five Child Mortality (U5CM) are considered differently from counties experiencing a lower risk of death.

Introduction
The burden of mortality in children has remained a key area of concern for nations and organizations in the world. The year 2018 recorded approximately 5.3 million children and infant deaths worldwide. The risk of under-five mortality in the WHO Africa region was 76 deaths per 1000 live births, which was eigth-times higher than the WHO European region [1]. This is far from ideal and is a worrying situation. The rate of mortality in Kenya in the same period was 46.7 deaths per 1000 live births. This was above the Millennium Development Goal target expected by 2015 of 33 deaths per 1000 live births, and the 2030 goal of 25 deaths per 1000 live births [2].
This national estimate, worrying as it is, is also accompanied by different concealed spatial variations [3] and, therefore, poses a complex problem unravelling the intricate localized or sub-regional variations in an attempt to better understand and offer feasible solutions to this burden. There exist various causes of child mortality in Kenya. The framework outlined by [4] provides a blue-print on studying mortality in third world countries.
Key sources of variation in child mortality in Africa include individual characteristics, such as wealth index, level of education, and nutrition [5]. The duration of breastfeeding, use of antenatal care, maternal health care services, birth intervals, and the age of the mother have also been known to be significant in determining the mortality of a child [6][7][8][9]. In addition, maternal education, a social economic factor, the presence of it and lack thereof has been shown to significantly affect the rate of survival of a child [10]. Other areas of concern on the determinants of child mortality include cultural practices [11].
The existence of these variations caused by the risk factors above are known to vary from region to region in a country. This suggests a presence of community level characteristics that influence health outcomes [12]. Such community level variables include income, place of residence, infrastructure, and region among others. These variables influence behaviors and patterns regarding child and maternal health care, and are significant in explaining child mortality [13]. Such disparity could be at the country level or county level. Urban areas have had lower risk of death for infants and children [14] in Ethiopia and yet higher odds of death in Nairobi [2] compared to rural areas.
It is, therefore, important to understand the distribution of these factors geographically and their influence in various locations. In order to understand these disparities, and eventually the risk factors, survival methods have been applied as a critical tool in the analysis of life data [14]. Cox models have been applied to model the time to occurrence of an event [15] and showed significant improvements compared to classical regression techniques [3].
The assumption of Cox models is that child survival is dependent on a baseline survival and certain risk factors; however, this is not often true in reality as survival data are dependent when clusters or locations are considered. This dependency introduces unobserved random effects (frailties) present at various levels, and suggests the presence of community level characteristics that influence health outcomes [12]. The existence of these effects caused either by a location, or a presence in certain population clusters leads to the use of spatial survival models to capture these unobserved effects, especially if they are geographical.
In spatial survival analysis, the survival of a child is assumed to be correlated if areas or points are close together, and it is possible to analyze data that are clustered when the clustering is a result of geographical regions or some other form of stratification [16]. The expected hazard rates will be more similar in neighboring regions, owing to underlying factors, such as access to health care services, which varies spatially. Studies, such as  Kazembe et al. (2007), in various regions in Africa emphasized the significance or improvement that spatial survival models have over and above the existing survival analysis models, particularly when the spatial heterogeneity is significant [3,[17][18][19].
The purpose of this study is to evaluate the effect of spatial variation on under-five mortality in Kenya. This is informed by the introduction of counties in 2010 and the delegation of health care functions to the counties. The extent of the disparities in the burden of child mortality between those counties is not known, and therefore it is important to investigate the differences to aid in county-based health care planning and intervention. This is done using a spatial Cox Proportional Hazards model using the Kenya Demographic and Health Survey, (2014) data set.

Data Source
The data analyzed in this study were sourced from the Kenya Demographic and Health Survey 2014 [20]. Authorization to use the data set was obtained through the DHS Portal, which contains a repository of the survey data. The Demographic and Health Survey is an initiative sponsored by the United States Agency for International Development (USAID) in partnership with other Kenyan research agencies, including the Kenya National Bureau of Statistics (KNBS), National Council for Population and Development (NCPD), Ministry of Health, Kenya Medical Research Institute (KEMRI), and National AIDS Control Council (NACC). The DHS program has been run in many nations to provide periodic updates, outlooks, and estimates of various indicators, such as maternal and child health and individual level information pertaining to the health of such individuals in specific cases. The data collected contains information about families, children, child bearing mothers, and other socioeconomic factors as relevant to help monitor the population and health status in Kenya.

Survey Design
The 2014 KDHS survey was the first of its kind to provide county level estimates since the promulgation of the New Constitution in 2010. County information was included in the dataset using a separate country shape file. The Kenyan agencies provided the personnel to conduct the survey. The survey used samples from the country's population and housing census estimates. Using a two stage sampling frame, Enumeration Areas (EAs) in the census served as the primary sampling unit. Clusters were selected in the first stage from the Enumeration Areas, while households were selected in the second stage. All mothers aged 15-49 years old were eligible respondents, and information about children born 5 years prior to the survey was obtained.

Variable Selection
The variables used in the study were pre-selected based on the existing literature on the significant determinants of child mortality. The variables ranged from demographic and socieconomic factors to variables specific to the mother and child. The study focused on the existence of spatial variations and the differences in child mortality across regions in the country. The demographic and socioeconomic variables selected included the sex of the child at birth, maternal age at birth, age of respondent at first birth, gender of the household head, Wealth Index, Highest Education level, and the type of place of residence. The geo-referenced regions were the counties and coordinates (displaced) provided. The primary outcome was the mortality of a child, defined as the time to death of a baby before his or her fifth birthday.

Statistical Analysis
Statistical analyses were performed using the R-software for statistical computing. Spatial survival analysis to estimate the spatial differences in mortality across the counties was performed using Intrinsic Conditional Autoregressive Models incorporated into the Cox proportional hazard model. In addition, the effects of the preselected variables were estimated using a Cox proportional hazards model.
We compared two survival analysis models, one with a spatial frailty assumption, while the other one had no frailty assumption. Model comparison was done through the Deviance Information Criterion. For an estimation of the model parameters, a Bayesian approach was used and assigned priors for the model priors and the distribution of the baseline survival function. An Intrinsic Conditional auto-regressive (ICAR) prior was assumed to model the spatial structure [3]. The results are presented as posterior estimates of the spatial variance and the covariates and the corresponding 95% Credible Intervals. For more details on the methods please refer to Appendix A.

Results
The number of children in each county is shown in Figure 1. The sample was representative of the children in each county, due to the sampling frame.  Table 1

Model Comparison
This section deals with the comparison of the two models fitted, the spatial differences in the risk of child mortality, and the results of the covariates considered. The Deviance Information Criterion was used to compare the performance of the two models, where a lower DIC indicated a better model fit on 856 degrees of freedom. The first model was fitted using the Proportional Hazards assumption on the child mortality data, where the response was the hazard of death dependent only on the baseline hazard and a set of covariates. The DIC value for this model (3384) was larger compared to the spatial model as shown in Table 1.
The second model was fitted with the spatial frailty term and had the lowest value of the Deviance Information Criterion (3344) and, therefore, a better fit. This model assumed unobserved variation (heterogeneity) at the geographical (county) regions in the country. There was a significant improvement brought about by the spatial model with frailties that accounted for randomness apart from the independent covariates. The Watanabe Akaike Information Criterion deals with the predictive power of the model, where lower values mean higher predictive power. The Spatial Cox proportional hazards model had the higher predictive power as shown in Table 1.

Goodness of Fit Tests
The Markov Chain Monte Carlo (MCMC) estimation deals with estimating model parameters in a Bayesian setting. The MCMC sampler explores the parameter space of a certain parameter, resulting from prior knowledge about the parameter and the likelihood of the observed values. To ensure mixing, the resulting chain selection of parameters results in a stationary distribution of the parameters. Trace plots-time plots of the Markov Chain-were used to assess the proper mixing of the parameters and were stationary, indicating convergence, and hence were reliable.
To assess the goodness of fit of the model, a Cox-Snell plot was used. Cox-Snell residuals; the residuals of the observed data points and the predicted values analogous to normal probability plots test the reliability of the model. The Cox-Snell plot showed hazard plots that were approximately straight with slope one, indicating a good model fit, as shown in Figure 2.

Model Results
The spatial frailty model had the best fit on the data. The posterior variance of the ICAR frailty; accounting for the variance due to the spatial effect is shown in Table 2. The mean was 0.2001, which was statistically significant with a 95% credibility interval (0.0578, 0.4649). On average, the spatial effect explained 20% of the heterogeneity variance. The statistically significant determinants of child mortality included the sex of the child, age of the respondent at first birth, gender of the household head, and whether a family had multiple sets of twins. These encompass the set of demographic risk factors associated with child survival, while the socio-economic factors associated with child survival included the highest level of education (secondary). The posterior mean for the sex of the child was −0.1304, and the median was −0.1315. The hazard ratio was exp(−0.1304) = 0.877, and the probability of survival was higher in female children by 12.23 percent compared to male children, holding all other predictors constant. The 95 percent credible interval of the hazard ratio was (0.7761, 0.9921).
The mean for the sex of the household head was −0.1550 and the median was −0.1579. Adjusting for the sex of the child, level of education, and age of the respondent at first birth; for a child born in female-headed households, the computed hazard was 0.8564, and therefore the probability of survival increased by 14.36 percent compared to male-headed households. The age of a respondent at first birth had a mean and median of 0.025. There was a positive association, i.e., an increased risk of death for children for an increase in age at first birth. There was also an increase of risk of death by 2.5 percent for a unit increase in age of a respondent at first birth. Families with multiple sets of twins (the first multiple of twins refers to the first set of twins born, while second multiple of twins refers to a second set of twins) had a high risk of child mortality. The mean was 0.4438, with a hazard ratio of 1.5586. Children born as a second set of twins had a higher risk of death by 55.86 percent compared to children born in single births.
The respondent's level of education was statistically significant at the secondary level. Respondents who had attained a secondary level of education in Kenya had a mean of −0.2982 and a hazard ratio of 0.7422; the risk of child mortality in respondents who had attained secondary education decreased by 25 percent compared to respondents who had not had any education at all. These results are shown in Table 3.  Table 2 shows the posterior frailties on the variance of the spatial term. The variance was 0.2001 and was significant at 0.05, the credible interval was (0.05776, 0.4649). Adjusting for the effect of the covariates, there were other unobserved spatially varying covariates relevant for child survival in Kenya.
In Figure 3, we can identify a cluster of counties with higher median frailties or a higher hazard (counties with yellow colors) located centrally in the country. This shows that these counties, apart from the covariates, have a spatial correlation, hence, the similarities in the hazard of death for a child. Children from these counties (Makueni, Machakos, Kiambu, Nyandarua, Nyeri, and Laikipia) were shown to have the highest risk of mortality adjusted for effects of covariates. Medium level frailties were identified as clustered around the Garissa, Tana River, Kilifi, and Lamu counties, Meru and Tharaka-Nithi counties, counties in Rift Valley (Nandi, Uasin Gishu, and Elgeyo Marakwet), and Narok and Kisii counties.
Counties in Western Kenya and Nyanza also had low frailties with spatial correlation (counties with purple color) related to death from other causes apart from the main effects of the independent variables. The highest number of counties, however, (counties with green color) had the same frailties and a similar hazard. These were counties in Northern Kenya, South Rift, and counties bordering Tanzania on the South West. The others were around the Mount Kenya region (Kirinyaga, Embu, and Murang'a) that showed a homogeneous risk of death of a child.

Discussion
The aim of the study was to bring out the spatial disparities on child mortality existing in the counties in Kenya. The spatial frailty proportional hazards model was used to determine the risk factors associated with under-five mortality in the country. Two models, the proportional hazard model without any frailty assumption and a frailty model assuming variations across space were compared.
The study reveals unobserved spatially varying covariates relevant for risk factors of child mortality in addition to the risk factors explaining child mortality. The ICAR spatial survival model was used to model these unobserved effects and to model the correlation and or clustering between these regions. This study, therefore, analyzed the time to death of a child using spatial survival models. The spatial model performed better compared to the non-spatial model, similar to [19].
Using the Akaike Information Criterion (AIC), the Cox Proportional Hazards model with spatial frailties performed better than the non-frailty model. The predictive ability of the spatial model was also higher compared to the non-spatial non-frailty model. The variation introduced spatially significantly improved the model, making it the superior model. The better fit model was therefore used together with the covariates to investigate the underlying factors contributing to child mortality.
The results show that patterns of child mortality in Kenya have a spatial structure, which is similar for the neighboring counties around Central Kenya, although majority of the counties appear to have similar frailties and, similar spatial distribution. The presence of unexplained variation, save for the main effects, was statistically significant and improved the model. This may be explained by similarities in access to healthcare and other amenities and the variance in counties, similar to [3].
After adjusting for the spatial structure, gender of the child, and respondent's age at birth, the probability of survival of a child decreased with an increase in the age at first birth. Multiple births were found to be significant risk factors associated with child mortality. The gender of the child (male children had a high risk of death) and whether a child was in a multiple birth (twins) were important risk factors (also found by [3]) associated with child mortality. The gender of the house-hold head was also found to be significant. The level of education, also found by [8], was an important risk factor for child mortality.
There is, therefore, an increased survival probability for children born to families whose respondents have attained a secondary level education. Children from mothers who had attained secondary education had higher chances of survival compared to children from mothers who had no education or only primary education. A primary level of education as well as a higher education level (beyond secondary) were not significant risk factors associated with child mortality. It is important, therefore, for most mothers to attain a secondary education level in the country for a reduction in the risk of child mortality.

Conclusions
This study brings out the spatial disparities that exist in the country on child mortality in Kenya. From the models used, the model incorporating spatial terms performed better; hence, there is a need to consider using the improved model to model key outcomes, such as the gender of the child, the age of the respondent at first birth, whether a child is in a multiple birth, gender of the household head and level of education, and other country indicators of health in the future.
The specific counties have mortality rates that are county-specific, although neighboring counties have similar hazards of death of a child indicating relationships of a county and its neighbour. It is important, therefore, to consider interventions that take into consideration the effect of where a child is born from (county) when providing intervention to reduce the risk of mortality.
In addition, such interventions should pay attention to the case where a child is born in a multiple birth and should provide special care and monitoring in families where such cases occur. Families with histories of multiple births, either twins or triplets and so on should have a flag on them, such as governmental, county, or community level initiatives that offer extra care for such children.
The important of education cannot be over-emphasized to ensure a child is kept safe from mortality. This ensures mothers have appropriate information on diets with more nutritional value, breastfeeding, immunization, and hygiene among others. Mothers with some level of education (secondary) are also more likely to have a form of income compared to mothers with no level of education. A level of education for a mother also implies that one is more likely to be married by a husband with similar or higher levels of education, which increases income for the overall household and, hence, significantly improves the probability of survival for a child. It is important, therefore, for most mothers to attain secondary education level in the country for a reduction in the risk of child mortality.
As the country aims to achieve the stipulations of the Millenium Development Goals, as it failed to hit the set targets for MDG 2015, and, in order to hit the targets for MDG 2030, the significant differences witnessed in counties should be considered at the policy level. All attempts to smoothly hit the 2030 target within the timelines should include a devolved approach, where counties spearhead the reduction in risk of mortality. Regional blocks for which mortality risks appear similar should be considered as an entity to ensure that the menace that is child mortality is dealt with.

Conflicts of Interest:
The authors declare no conflict of interest.

Abbreviations
The following abbreviations are used in this manuscript: The event of interest was in modelling survival times: the time taken until an event of death occurred for an individual child. Assuming a sample of size N children aged between 0 to 59 months, the survival information of a child is (t i , δ i ), i ∈ 1, · · · , N where t i ∈ 1, · · · , 60 is the child's survival time in months and a status variable δ i = 1 if the child is dead and δ i = 0 if the child is still alive [3]. Further suppose that the child is a part of a sample from P counties, the number of children in county j is n i where j = 1, · · · , n i and

. Cox Proportional Hazards Model
The proportional hazards model from [15] has a key assumption that the hazard of death for an individual is proportional to the hazard for any other individual. The hazard model dependent on the baseline hazard and a set of covariates is shown as where t is the survival time, h(t) is the hazard function, β T is a parameters vector of px1 dimension, and x ij is a vector with the values of the covariates in individual i from county j(1xp) dimension, h 0 is the baseline hazard function; while e β i denotes the hazard ratios (H.R).
The determinant of this matrix is 0, but with some data from N it is positive definite. The advantage of the ICAR model from the usual CAR model is the reduction of computation time, and capturing the spatial dependence mainly by the distance between the values of the neighboring regions.

Appendix A.5. Bayesian Approach
For an estimation of the model parameters, a Bayesian approach is used and assigned priors for the model priors, and the distribution of the baseline survival function. An Intrinsic Conditional auto-regressive (ICAR) prior was assumed to model the spatial structure [3]. The prior models the mean for each area φ i , which is conditional on areas neighboring it; is normally distributed with mean equal to the average of the neighboring areas φ i and variance is inversely proportional to the number of neighbors d i as above.
For the semi-parametric survival function, the baseline survival function is modeled using a Transformed Bernstein Polynomial (TBP) prior, usually centered around a given parametric family and selects only smooth densities [22]. The prior TBP L (α, S θ (·)), where α > 0 and S θ are parameters of a Dirichlet Process, defined as S 0 (t) = L ∑ j=1 w j I(S θ (t)|j, L − j + 1), where W L ∼ Dirichlet(α, · · · , α), and W L = (w 1 . · · · , w L ) T is a vector of positive weights, I(·|a, b) denotes a beta cumulative distribution function with (a, b) as parameters and S θ (·) : θ ∈ Ω is a parametric family of survival functions. The log-logistic S θ (t) = (1 + (e θ 1 t) exp(θ 2 ) ) −1 (A8) is chosen as the prior specification for the baseline survival function in this study. Vague normal priors are chosen for the regression coefficients, and a gamma prior τ −2 ∼ Γ(a τ , b τ ) for the precision parameter [23]. Posterior inference on the parameters were computed using Markov Chain Monte Carlo iterations carried out through a Bayesian approach [23]. The likelihood function based on (w L , θ, β, φ) is given by; [Sx ij (a ij ) − Sx ij (b ij )] I(a ij ≤b ij ) f x ij (a ij ) I(a ij =b ij ) (A9) The posterior distribution given as p(w L , θ, β, φ) = p(θ, β, φ)L(w L , θ, β, φ) where p(θ, β, φ) is the prior distribution of the parameters and L(w L , θ, β, φ) is the likelihood function obtained from the observed values. Model comparison is done using the Deviance Information Criterion (DIC). Analysis was carried out in the R-software for statistics with the spBayesSurv package. A burn-in period of 5000 iterations was considered, and displayed after every 1000 saved iterates and run for 615 s on an HP Intel Core i3 Laptop computer. Small values of DIC indicated better performing models.
The flow diagram for the model implementation is shown in Figure A1.


Focus on the Chicken Manure Power Generation in China: How to Promote the Biomass Waste Industry?

With the vigorous growth of animal husbandry, animal feces in the agriculture sector gradually deteriorate the environment. The chicken manure power generation is becoming viable and useful for energy conversion to comply with the context of environmental protection in China. Based on resource endowments and technical conditions, this paper studies the current situation of chicken manure power generation in China. Combined with the policy environment, the research conducts a PEST-SWOT matrix analysis to thoroughly look into the strengths and weaknesses, the opportunities and challenges. Then, the paper analyzes the distribution of chicken manure and gives some solutions from respects of government regulatory behavior, industrial-organizational behavior and corporation strategic behavior. Finally, it is concluded that: 1) the government should strengthen policy support by actively improving the subsidy mechanism and lowering the threshold of financing and credit; 2) enterprises should focus on improving power generation technology and boiler treatment technology.

Introduction
At present, China is striving to reach the peak of carbon dioxide emissions by 2030, and to achieve carbon neutrality by 2060. China is less than 10 years away from achieving the peak carbon goal, and only 30 years from the peak carbon goal to the carbon neutral goal, compared with developed countries, China has an extremely difficult task to achieve the peak carbon and carbon neutral goals. The demand for energy growth for economic development and the pressure for carbon reduction coexist, and the development of clean energy is indispensable in improving the energy supply structure and building a national ecological security barrier. Biomass energy, the only renewable energy source, that can be transported, has received extensive attention because of the rich resources and wide geographical distribution [1]. It is reported by the China National Renewable Energy Centre that in 2015, China's biomass power installed capacity reached 10.6 million kilowatts, second only to that of the United States (15.9 million kilowatts) and Brazil (11 million kilowatts) [2]. China's biomass power generation grid-connected capacity reached 29.52 million kilowatts at the end of 2020, up 22.6% year-on-year; the annual installed capacity was 5.43 million kilowatts, ranking the first in the world. Meanwhile, due to the development of the chicken industry, the environmental problems caused by chicken manure have become serious increasingly [3]. The excrement of the chicken farm cannot be managed effectively, which causes a series of problems to the surroundings. More importantly, the improper disposal of chicken manure on farms can cause pollution to the ground, groundwater and soil, which would damage the ecological environment at last.
Chicken manure is a high-quality raw material for power generation and its production in China is huge. However, the history of chicken manure power generation in China is relatively short. Noted that the first combustion power plant and biogas power plant using chicken manure as raw materials were put into operation in April 2009 [4]. So far, the installed capacity of China's grid-connected power plant using chicken manure has been less than 100 MW. The handling capacity of chicken manure per year is less than 1% of its total excretion. It can be seen that the utilization rate of chicken manure is at a superficial level, which means chicken manure power generation has much more potential for development.
In order to promote the rapid development of clean energy, the Chinese government has issued many policies since 2013. The National Development and Reform Commission issued the "Revolution Strategy for Energy Production and Consumption" (2016-2030) in 2016, and clarified that from 2021-2030, new energy demand will be met mainly by clean energy. Total energy consumption will be controlled within 6 billion tons of standard coal. The proportion of non-fossil energy in total energy consumption will reach about 20%, and carbon dioxide emissions per unit of GDP will drop by 60% -65% compared to 2005. Therefore, the clean energy industry will face major challenges. Although solar energy and wind energy are mainstream clean energy, wind curtailment and solar curtailment have frequently occurred in China for more than ten years [5]. In contrast, chicken manure is not only inexhaustible but also stable to provide electrical energy [6]. The development of chicken manure power generation can bring considerable effective environmental benefits as well as economic benefits because of its obvious role in pollution abatement. For all that, as an emerging in-dustry, chicken manure power generation sector still faces lots of problems to be solved, such as technology innovation and acquisition of raw material and so on [7].
In the PEST-SWOT matrix, strengths, weaknesses, opportunities and threats of chicken manure power generation are analyzed from four aspects: policy, economy, society and technology. According to the requirements of clean energy, it is necessary to consider the factors of supply and demand, technology and environment to make a judgment on the development direction of chicken manure power generation. Through the analysis, this paper puts forward some solutions on government regulatory behavior and industrial-organizational behavior. In the end, combining the comparison of two kinds of power generation technologies and the distribution of chicken manure in various regions of China, the corporation strategic solutions are given.

Characteristics of Chicken Manure
The test of the combustion characteristic shows that the mixture of chicken manure and rice husks can produce 10.45 MJ/kg lower heating value, half of the raw coal [8]. And the carbon-nitrogen ratio of chicken manure is 15.6 to 1, which is very high. The calorific value of biogas is 23 -24.3 MJ/m 3 so that 1 m 3 of biogas is equivalent to 3.3 kg of raw coal [9].

Production and Distribution of Chicken Manure
The chicken manure emission factors are from First National Survey of Pollution Sources of Livestock and Poultry Livestock Production Sewage Coefficient Manual, in which layers are 0.12 kg/day and broilers are 0.14 kg/day. Based on the number of layers and broilers in the China Agricultural Statistics 2018, it can be calculated that chicken manure production reached 287.31 million tons in the country. Chicken manure volume and distribution density in each province (municipal city, autonomous region) are displayed in Figure 1.
Chicken manure volume is calculated according to the following formula [10]: W F : chicken manure annual emission (million tons); H L : the number of layer stock and broiler stock (ten thousand); F D : the chicken manure emission factor (kg/day/per capita); T D : feeding days.
In the last decade, China's chicken industry has developed rapidly from the scale of farming to the level of production.   Compared to 2006, the total number of farms and large-scale farms increased significantly in 2017, as can be seen in Figure 2. This shows that the chicken industry has an increasingly high degree of intensification. The number of large-scale farms with an annual chicken slaughter above 500,000 in each province (municipal city, autonomous region) is displayed in Figure 3.

Combustion Power Generation Using Chicken Manure
The boiler uses the combustion technology of circulating fluidized bed and adopts low-temperature combustion and segmented air supply to effectively reduce the NO x and SO 2 generated during combustion, which has gained a great advantage in environmental protection [4]. In addition, the combustion by-product of the boiler contains high organic substances such as phosphorus and potassium, which can be used as high-quality compound fertilizer [11]. The value chain of combustion power generation using chicken manure is displayed in Figure 4.

Biogas Power Generation Using Chicken Manure
Biogas power generation uses methane produced by mixing chicken manure with farm sewage and crop straw as fuel. And per kilogram of chicken manure can produce 0.37 m 3 biogas [12]. The electricity can be used in farms and can be delivered to the grid. At the same time, the thermal energy generated during the electricity generation process will be used for the fermentation system to keep warm and provide heat to the chicken farm in winter [13]. The value chain of chicken manure biogas power generation is displayed in Figure 5.

Installed Capacity
According to the data released by the National Energy Administration, in 2020, China's biomass power generation grid-connected installed capacity reached 29.52 million kilowatts, an increase of 22.6% [14]. However, the installed capacity of chicken manure power generation is very small, and the current chicken  manure combustion power generation and chicken manure biogas power generation is only tens of thousands of kilowatts, and chicken manure is a kind of manure that produces less gas than other livestock manure [14]. At present, there are three main ways to recycle chicken manure in the world: energy, fertilizer and feed. In western developed countries, power generation is an important way of using chicken manure. In China, however, it is fertilizer, and the proportion of power generation is very small. It can be learnt in Figure 6 [15].

Methodology: PEST-SWOT Analysis
Combining the industry status of chicken manure power generation, this article analyzes the strengths, weaknesses, opportunities and threats of the industry from the four aspects of policy (P), economy (E), society (S) and technology (T). The analysis matrix is displayed in Figure 7.

Policy Opportunities
In the "13th Five-Year Plan for Renewable Energy Development", it was proposed that "improve the price, taxation and other preferential policies to support the development of biomass energy". Also, since 2010, the government has successively issued the "National Development and Reform Commission Circular on the Management of Biomass Power Generation Project Construction", "Guidance on Promoting the Development of Biomass Energy Heating" (2017) and so on. All of these policies can clarify the status of the biomass power generation industry and promote the development of chicken manure power generation enterprises directly [16] [17] [18].   Firstly, China has not yet set emission standards specifically for the biomass power generation industry. The current standards for the emissions from agricultural and forestry biomass power generation are implemented according to that of the coal-fired power generation, which is unfair for environmentally significant biomass power generation. Secondly, the biomass power companies have invested significant amounts of capital to achieve ultra-low emissions, but they are unable to enjoy electricity price subsidies in time [19]. Moreover, biomass power generation is plagued by technological lags, and many companies are unable to reach the continuously updated ultra-low emission level, which makes them unable to obtain subsidies for reducing emissions [20].

Policy Threats 1) Emission standards are not clear
2) Loan discount policy is not perfect At present, fewer projects can enjoy the benefits of the loan interest subsidy policy. Chicken manure power generation is an emerging industry encouraged by the government and requires a large amount of capital. However, commercial banks cannot provide lower loan rates than conventional ones, which makes the financial situation of chicken manure power generation projects more dangerous [21].

Economic Strengths
Chicken manure is abundant and inexpensive, which can achieve higher economic benefits than itself when it is energized [22]. Biogas power generation is the most common method of converting manure into energy. The "chicken-fertilizer-methane-electricity" recycling industry chain can achieve excellent economic benefits, and it can also save a large number of sewage charges [23]. In another way, the ash left by combustion power generation is high-quality potassium and phosphate fertilizer that can be used directly, which eliminates the costs of ash delivery and storage [24].

Economic Weaknesses
Although the chicken manure production is massive in China, the distribution is not concentrated. The high transportation cost of chicken manure is an essential factor that restricts the power generation [25]. Chicken manure power generation needs enormous investment, and capital recovery is slow. Moreover, investment in emission reduction is huge [26].

Economic Opportunities
1) Industrial economy upgrade At present, the environmental carrying capacity of China has approached the upper limit, and new ways are urgently needed to promote low-carbon development. The "Revolution Strategy for Energy Production and Consumption" (2016-2030) clear that, in combination with new-type urbanization and agricultural modernization, China will expand the scope of electricity use, give priority to the use of renewable energy power, simultaneously promote electrification and information technology construction, and open up a new era of future-oriented energy consumption [27]. As an important project for capacity reduction, chicken manure power generation can promote the sound development of circular economy.
2) Carbon emission trading The greenhouse gas produced by burning chicken manure is about 25% of it produced by burning coal [4]. Moreover, greenhouse gas emissions from biogas power generation are close to zero. Most importantly, the project can also obtain the Clean Development Mechanism (CDM) income. For example, Sunner Company obtains 100 million yuan by processing 700,000 tons of chicken manure annually [28]; Shandong Minhe Stock Co., Ltd. also obtained CDM transaction benefit from biogas power generation [29]. Chicken manure power generation is less capital intensive than traditional power generation projects so that investors are more cautious about investing in the project. In particular, it is often located in rural areas or suburbs, where financing is difficult [30].

Economic Threats 1) Investment and financing difficulties
2) Transportation and supply bottlenecks Compared with coal-fired power generation, biomass power generation projects are environmentally friendly and renewable, but the collection costs are huge [31]. Besides, the quality and supply efficiency of raw materials have restricted the development of China's biomass energy industry to some extent [32].

Social-Natural Opportunities 1) Better environmental awareness
With the advancement of society, citizens' awareness of emission reduction has gradually increased. Besides, enterprises have begun to use energy-saving equipment to reduce electricity consumption, and the government has also adopted restrictions on high-energy-consuming projects, which has contributed to the promotion of chicken manure power generation with low-carbon [33].

2) Providing employment opportunity
In recent years, the trend of rural migrant workers returning home for employment or entrepreneurship is noticeable. The development of the chicken manure power generation industry is conducive to promoting the development of the chicken industry, which can provide more employment opportunities for farmers [34].

Social-Natural Threats 1) Low cognition degree of society
Since chicken manure power generation has just emerged in China, and the media has reported very little on it. People are surprised by this type of power generation, and few investors have noticed it, which is an important reason why it has not been widely promoted [35].

2) Secondary pollution
At present, there is no standardized system for the operation of chicken manure power generation project in China, and secondary pollution exists in the production process. On the one hand, pollutants may leak during transportation. On the other hand, how to make full use of the large amount of biogas and ash produced by combustion is a problem to be solved [36].

Technological Strengths 1) Combustion power generation technology
Compared to conventional coal power, combustion power generation does not require a crushing and milling system. It only requires some conventional system, such as the combustion system, circulating cooling water system and thermal system. However, fuel delivery and ash collection are the main concerns that need attention. It is worth mentioning that the ash collection adopts two-stage dust removal technology, which can collect nearly 99% of the dust [37].
2) Chicken manure fermentation technology A mixture per kilogram of chicken manure and straw produces 0.37 m 3 of biogas with a methane content of 54%, and raw material digestibility is about 65%. It can achieve higher natural gas production than other biomass fermentation materials [38].

Technological Weaknesses
At present, there are certain technical defects in the boilers developed in China. The slag tends to block the discharge port, and the fluidization boiler sometimes fails to cause coking, resulting in insufficient output. Moreover, the accumulation of ash on the surface of the back-end flue is severe, considering the special fuel characteristics and smoke composition of chicken manure [39].
Due to seasonal changes and weather changes, the water content of chicken manure is variable, and this change reduces the efficiency of starter culture. Besides, biogas fermentation temperature is mainly at medium-temperature (about 37˚C). However, the temperature in the north of China is generally low in winter and spring, which significantly limits the gas production rate of biogas [40].

Results and Discussion
Based on the introduction of the industry's status and the PEST-SWOT matrix analysis, in the chicken manure power generation sector, it is demonstrated that some practical problems need us to solve. In order to promote the positive development of the sector, this section gives some solutions in three aspects: government regulatory behavior, industrial-organizational behavior and corporation strategic behavior. As displayed in Figure 8.

Reduction of the Financing Credit Threshold
Government agencies should lower the threshold for financing credit and establish a complete competition mechanism between the same industry, which can implement a differentiated credit system. In order to make direct financing become the main channel for chicken manure power generation companies, a comprehensive diversified and multi-level capital market system should be established. Also, it is proposed to form a new policy financial organization to provide financing services for strategic emerging sectors.

Establishment of the Power Generation Demonstration Site
The demonstration and promotion of this power generation technology have the nature of quasi-public products. Therefore, it is advisable to establish chicken manure power generation demonstration sites in provinces with a well-developed poultry industry, in order to solve the difficulty of technology promotion and the high cost of operation. It also helps promote the vigorous development of farming entities and other investment entities in the vicinity of power generation projects.

Setting of the Emission Standards
It is recommended to issue environmental emission standards for the chicken manure power generation sector. Due to the specificity of the components contained in chicken manure, it should not be measured by coal-fired emission standards. For the long-term development of the sector, appropriate emission standards should be formulated according to the level of development. Such an approach can avoid unreasonable losses caused by policies and taxes. In addition, due to seasonal changes, some characteristics of chicken manure composition and gas emissions may be slightly different. To reduce the tax pressure of those companies, it is suggested that punishment for environmental pollution should be decoupled from the Value Added Tax (VAT) rebate.

Construction of Raw Material Recovery System
The problem of a raw material bottleneck in the development of chicken manure power generation sector can be solved through supply chain optimization.
The main purpose of supply chain optimization is to reduce the cost of raw material supply and improve the quality of raw material supply. For the first model described in 3.2.4, e-commerce could be introduced. Farmers can share chicken manure quality standards and prices and other information through the Internet platform to reduce the benefits for brokers. For the second model, it is possible to learn from the practice of Kaidi Eco and establish a storage station in cooperation with the village committee to reduce the construction and operation costs of the storage station. For the third mode, farmers can be encouraged to collect and transport chicken manure to enterprises by appropriately increasing the price of chicken manure. In a word, the key to supply chain path optimiza-tion is that enterprises should master the dominance of the supply chain and can enhance the enthusiasm of all parties involved in the storage and transportation of raw materials.

Construction of Distributed Generation System
With the advancement of urbanization and distributed energy technology, distributed power generation based on biomass power has great potential. Most chicken farms are built in rural areas or around towns near the countryside, where per capita resource occupation and energy demand have increased significantly. Chicken manure power generation is in line with the characteristics of integrated generation and utilization of distributed power generation. Based on the comprehensive utilization of chicken manure biomass, it can constitute a distributed renewable energy system combining multiple uses such as gas production, power generation, and heating. Due to sufficient raw materials, the system is stable enough to support the electricity demand of residents in rural or small-medium cities, which also can reduce environmental protection pressure.

Strategic Options
This section compares the characteristics of the two types of power generation, especially the raw material consumption and emission levels. According to the distribution of raw materials in the country, the most efficient power generation modes in each region are given.
1) Comparison of two types of power generation Based on the industry status and PEST-SWOT matrix analysis, the characteristics of the two types of power generation modes can be derived, as displayed in Table 1.
2) Selection of strategic scheme The grading standard for the total amount and distribution density of chicken manure are displayed in Table 2, which was determined by using the "three-equidistant intervals method" [10]. High cost for emission reduction; Poor fuel adaptability; Large initial investment.

Gas power generation
The fuel is fermented to produce biogas, which is purified and burned, and then drives an internal combustion engine or a gas turbine.  The total amount of chicken manure (Ten thousand tons) ≥1400 320 -1400 ≤320 The distribution density of chicken manure (kg/hm 2 ) ≥800 300 -800 ≤300 Taking the total amount of chicken manure as the abscissa and the distribution density of chicken manure as the ordinate to make the matrix diagram displayed in Figure 9. The total amount of chicken manure and its distribution density is displayed in Figure 1. According to the grading standard given in Table 2, 31 provinces (municipal cities, autonomous regions) in the country are divided into 9 parts and are listed in Figure 9.
According to the matrix, this study divides the distribution of chicken manure in 31 provinces (municipal cities, autonomous regions) into three major types. And the strategic choice of the enterprise is displayed in Figure 10.
The first category is the areas with a high abundance of chicken manure, including high quantity-high density areas, high quantity-medium density areas and medium quantity-high density areas, totaling ten provinces, as displayed in Figure 9. In these ten provinces, the total chicken manure was 202 million tons in 2017, accounting for 70.30% of the national total, and the chicken manure distribution density was 1047.42 kg/hm 2 in 2017, which is 2.48 times higher than the national average density. This type of area can be used as a critical development area for chicken manure power generation throughout the country, where high-density areas are suitable for the development of combustion power generation or large-scale biogas power plants.
The second category is the areas with a generally available abundance of chicken manure, including high quantity-low density areas, medium quantity-middle density areas, and low quantity-high density areas with a total of eight provinces. In these eight provinces, the total amount was 83.72 million tons in 2017, accounting for 29.14% of the national total, and the chicken manure availability distribution density was 148.27 kg/hm 2 , which was 49.3% of the national average density. High density areas in this type of category, such as Tianjin, have higher requirements for environmental protection. The biogas power generation project is carried out in the area, which is conducive to improving the environmental management of the farm. Moreover, other provinces in this category can be developed as biogas power generation projects. Coupled power generation can also be considered if boiler conditions permit.
The third category is the areas with a low abundance of chicken manure, including medium quantity-low density areas, low quantity-middle density areas, with a total of thirteen provinces. In these thirteen provinces, the total amount of chicken manure obtained was 42.11million tons in 2017, accounting for 14.66% of the national total, and the chicken manure distribution density was 61. 28    density. Considering the development of new energy sources, this type of area must fully consider the availability of chicken manure. When conditions permit, coupled power generation of chicken manure can be developed [41].

Technological Innovation 1) Increase of the boiler output
Due to the melting characteristics of chicken manure fuel ash, the combustion temperature in the furnace is from 100˚C to 150˚C, which is lower than that of conventional coal-fired boilers [42]. Therefore, if the chicken-combustion boiler has the same heating area as the coal-fired boiler, it will inevitably lead to the insufficient output. Comparing the state before and after the transformation of the boilers, the removal of the local castable in the furnace can significantly increase the heating area of the furnace. Therefore, the furnace evaporating heat Energy and Power Engineering receiving area of the chicken manure boiler is designed more than the conventional coal-fired circulating fluidized bed boiler, so that the design output can be achieved.
2) Fermenter thermostatic system design In order to ensure that the biogas fermenter can maintain a constant temperature and produce a sufficient amount of gas in winter, it is necessary to design a constant temperature system. Installation of the biogas fermenter in a sealed building with an air interlayer. Due to the relatively small thermal conductivity of air, it is suitable for heat preservation. The "gas-heat-constant temperature-gas" process is displayed in Figure 11. The waste heat generated by the biogas power generation heats the CSTR reactor and the secondary fermentation reactor through an external heat exchanger, thereby maintaining a high level of medium temperature gas production efficiency.

Conclusions
Chicken manure power generation is a vital renewable energy resource, featuring in green, low-carbon, clean and renewable. The development of chicken manure power generation is a crucial measure to improve environmental quality and develop a circular economy, which is of great significance for achieving the goal of low carbon development and energy transformation. Based on the analysis of this paper, the following main conclusions are drawn: Firstly, in China, chicken manure power generation sector is still in its early stages of development, but related power generation technologies are almost mature. At present, many chicken manure generators are in operation and have excellent economic and environmental benefits. With the rapid expansion of the scale of chicken industry and the continuous reduction of the cost of collecting chicken manure, the promotion of the chicken manure power generation industry will be successful. Through the PEST-SWOT matrix, this paper conducts a comprehensive analysis of combustion power generation and biogas power generation industry in terms of policies, economy, society and technology. China has paid more and more attention to the chicken manure power generation in-dustry, especially in terms of electricity price subsidies and external construction environment optimization. Moreover, China is in the stage of optimization and upgrading of industrial structure. The green power generation industry has a good opportunity for development, and CDM transaction revenue also provides important economic benefits.
Secondly, this article provides some solutions which are suitable for China. To begin with, the government should strengthen policy support by actively improving the subsidy mechanism and lowering the threshold of financing and credit. Through the establishment of targeted discharge standards of chicken manure power generation and the construction of raw material recovery systems and demonstration base, the standard of the industry can be achieved. Then, professionals are trained through school-enterprise cooperation. Also, to achieve the purpose of improving power generation efficiency, enterprises should focus on improving power generation technology and boiler treatment technology, such as the design of fermentation tank thermostatic system. Finally, according to the distribution of chicken manure resources in China, a reasonable enterprise strategy selection scheme is developed. These methods enable us to achieve the highest economic, social and environmental benefits.
Lastly, there are still some problems with chicken manure power generation. The problem of severe dust accumulation in the flue and short operating cycle has not been resolved. It is necessary to analyze the crystallization temperature of chicken manure in light of its chemical characteristics and to design-related technologies. In addition, the standards for the catalyst of chicken manure fermentation have not yet formed. Thus, it is urgent to make improvement in relevant technologies.


Role Of Financial Intermediaries in Shadow Schemes: Risk-Based Approach

The article is devoted to the study of the role of financial intermediaries in the shadow sector of the economy. The main purpose of the article is to analyze and systematize the work of scientists to assess the risk of shadow transactions with the participation of financial intermediaries. The results of the analysis of scientific publications on these issues show a wide variety of approaches to the study of these problems. Significant differences in the functioning of the financial, banking, insurance and investment markets of different countries have led to the need to develop and implement their own methodologies for assessing the risk of participation of financial intermediaries in shadow transactions at the state level. The paper summarizes the methodological approaches to assessing the risk of participation of financial intermediaries in shadow transactions in some countries. The methodological tools of the analysis are the methods of analysis and synthesis, generalization and comparative analysis. The object of the study are insurance companies, banking institutions and investment funds - participants in shadow schemes of income concealment. The article summarizes the most typical schemes of income concealment in terms of individual groups of financial intermediaries. The conclusion about the dual nature of banking institutions in these operations is made: 1) banking institutions as a "buffer", and sometimes a direct participant on the way to the legalization of shadow income; 2) banking institutions as a subject of primary financial monitoring and control. Insurance operations that are subject to enhanced control by regulatory authorities include: pseudo-insurance operations; concluding an insurance contract with subsequent receipt of compensation for the occurrence of a fictitious insured event; concluding an insurance contract with an inflated insurance value of the object. Based on the analysis of transactions with signs of fictitiousness, which are realized on the investment market of Ukraine, the conclusion about the important role of technical securities in operations on shadowing of incomes and their further legalization is made. The results of the study can be useful for public financial monitoring and control bodies in terms of identifying transactions that could potentially pose a threat to the formal sector of the economy and developing measures to prevent them.

Introduction
Given the high level of shadowing of the economy in most countries, one of the challenges for international and government institutions is to develop a methodology for estimating the likelihood of shadow entities engaging in economic activities. The stability of the economic system and its attractiveness to international partners depend on the availability of effective tools for assessing the risk of participation of economic entities in shadow operations.
To date, international scientists and public authorities in some countries have developed a number of approaches to assessing the risk of shadowing the economy, which differ in the set of indicators and entities in terms of which the assessment is conducted (Almenar et. al., 2020;Del'Anno, 2003;Doyle et. al., 2016;Mikulić, 2021;Shumska & Nezhyvenko, 2013). Thus, the shadow sector of the economy in different countries differs in the list of drivers of its ex Del'Anno existence, the totality of which is determined by the peculiarities of their economic systems, the level of international cooperation, efficiency of state regulatory authorities, and the level of tax morale in society.

Literature Review
The high level of shadowing in most countries leads to the need to implement a methodology for assessing the risk of shadowing of income at the state level, which would take into account the structure of the financial market, the degree of development of individual sectors, the role of financial intermediaries in the shadow economy. Significant differences in the functioning of the shadow sector of the economy lead to a significant variety of approaches to assessing the risk of shadowing of income, money laundering and territorial financing ( Table 1). In addition to government institutions, the assessment of the risk of shadowing the economy attracts the attention of a large number of scientists in different countries. Investigating the role of banking institutions in shadow operation, Favarel-Garrigues et al. (2007) emphasizes the responsibility of bank employees who work with customers for risk assessment in the implementation of operations on account opening, savings, withdrawals and transfers. According to Johnson (2003), the assessment of the risk of shadowing of income with the participation of banking institutions should be based on the intuition of the bank employee and his prudence. If a client is assigned to a group of clients with high or low risk, a bank employee must suspend the implementation of a banking transaction with this client.
At the same time, theoretical and methodological developments in the risk of participation of investment funds, insurance companies and other financial intermediaries are virtually absent.

Methodology and research methods
The methodological basis of the paper is the methods of generalization and synthesis, comparative analysis.

Results
The results of the analysis indicate the availability of a wide range of tools for assessing the risk of shadowing the economy. At the same time, most approaches focus on individual operations or activities by sector of the economy, while ignoring the study of the likelihood of financial intermediaries participating in shadow transactions.
Within the study, the risk of financial intermediaries' participation in shadow transactions will be understood as the probability of carrying out shadow transactions with the direct or indirect participation of financial intermediaries.
The probability of financial transactions is considered in terms of the following intermediaries: In the context of digitalization of the economy, the pace of which under the influence of the Covid-19 pandemic has accelerated significantly, in the global financial market due to most payments in non-cash form there is a significant reduction in cash turnover (Tiutiunyk et al., 2021). Given these trends, criminals who receive income from shady or illegal activities are forced to legalize their income by legalizing it in the financial system, while minimizing suspicions about the sources of their receipt.
In general, the process of legalization of illicit income through the banking sector can be affected in the form of a scheme (Figure 1).

Figure 1. Legalization of illegally obtained income through banking institutions
Source: Compiled by the author.
Revenues received in the shadow sector of the economy go to the country's financial system through their placement in banking institutions. The next stage is the masking of illegally obtained income and their maximum separation from the illegal source of their origin.

Placement of funds
Legalization of illegally obtained income Changing signs of cash flows Cash flow in the banking sector One of the most common operations in this direction is lending to fictitious companies, followed by payment for the services of third parties. The purchase of certain tangible or financial assets at the expense of illegally obtained income contributes to their faster entry into the real sector of the country's economy with their subsequent legalization. Relaxation of these transactions is carried out mainly in order to avoid suspicions of law enforcement agencies about the legality of the source of these revenues.
Quite often the opening of single accounts contributes to the achievement of these goals. Thus, the subjects of the shadow economy with the help of a newly established firm (usually operates for less than a year) open accounts in various commercial banks, make illegally received income on deposit (in small shares), and then carry out operations to legalize these funds.
No less common are transactions to pay obligations under non-existent contracts. Thus, economic entities by falsifying documents, which indicate the data of non-existent transactions (terms of the contract and the amount of payment) transfer significant amounts of money through banks. The subject of the contract, as a rule, are certain services, less tangible assets.
A common tool for laundering illegally obtained income is the accrual of salary payments or remuneration in favor of an individual or legal entity through banking institutions. Implementation of these operations allows for tax evasion and legalization of shadow income. Thus, based on the results of the analysis, it can be concluded that among all operations of insurance companies, more intensive control by regulatory authorities should be subject to: pseudo-insurance operations; concluding an insurance contract with subsequent receipt of compensation for the occurrence of a fictitious insured event; concluding an insurance contract with an inflated insurance value of the object.
Reinsurance operations play a key role in shadow transactions with financial intermediaries. These operations serve as an effective tool for concealing income, reducing the transparency of insurance transactions (Fig. 2).

Figure 2. The relationship of insurance and reinsurance operations in the shadow schemes of withdrawal
Source: Compiled by the author.
The initial stage of shadowing of income with the participation of insurance companies is the conclusion of an insurance contract, the subject of which is the transfer of responsibility for risk to the insurance company. In accordance with the terms of this agreement, the insurance company assumes all risks and in the event of an insured event undertakes to pay compensation in exchange for insurance payments received from the insured. Almost 70% of premiums paid under insurance contracts are transferred to non-residents for reinsurance. This is due to the implementation of insurance companies' pseudo-insurance operations. At the same time, a significant part of these transactions is implemented by companies that do not have the legal right to carry them out. These trends confirm the hypothesis of active use of pseudo-investment operations in the process of legalization of illegally obtained income.
2) the risk of shadow transactions with the participation of investment market participants. Today, economic entities use a wide range of investment instruments of shadow withdrawal, which can be divided into three groups: direct, portfolio and financial (Christensen & Kapoor, 2004;Mara, 2021).
1. Portfolio investments include funds invested in debt or equity securities. Today, a common practice in most countries of the world is the shadowing of income and their further legalization through investment operations in derivative securities. Portfolio investment creates additional opportunities for the withdrawal of profits to the recipient's country of origin with their subsequent shadowing or tax evasion.
2. Transactions with technical securities play an important role among investment instruments of income shadowing and their further legalization. Quite often the value of such securities is not secured by real assets, and therefore is subject to manipulation by participants in the shadow sector of the economy through the implementation of the following schemes: 3. Sale of technical securities at a value several times lower than their nominal value. After that, these securities can be sold on the market at inflated prices.
4. Issuance of fictitious (non-existent) securities. Thus, according to the State Tax Service of Ukraine, in the country more than 420 economic entities carry out operations with counterfeit bills whose nominal value exceeds UAH 16.5 billion.
5. Carrying out transactions with technical securities in order to increase costs and thus minimize corporate income tax liabilities. The share of these transactions in the securities market in recent years exceeds 15%.
6. Issue of technical securities in order to increase the authorized capital of such a company. These economic entities form their own authorized capital with the subsequent withdrawal of funds through the purchase of technical securities.
7. Realization by domestic economic entities of a non-resident of technical securities at a reduced value with their subsequent purchase from a non-resident at a price above the market price. The implementation of these operations allows you to withdraw funds abroad, including in offshore countries. In addition to the direct withdrawal of funds abroad, these transactions contribute to the tax evasion of economic entities.

Conclusions
The constant growth of the level of the shadow economy in Ukraine, highlights the need for a more comprehensive study of all possible tools for concealing income and identifying participants in these schemes.
The results of the analysis show the active participation of financial intermediaries in shadow schemes of income concealment. These entities act as intermediaries and direct participants in these transactions. Thus, strengthening control over their activities, closer attention by regulatory authorities to operations carried out with their direct participation is a necessary prerequisite for the implementation of policies to de-shadow the economy and prevent money laundering.


Public revenue in India: Trend and effect

Public revenue is a major component of budget shows the manner in which revenue is collected during a financial year by government to boost economic growth. The success of government plan for the growth and development of a country depends on the source and size of public revenue. In India Public revenue acts as government’s most important economic and fiscal policy tool in controlling money supply and maintaining general price level. It is not only important for the corporate but for individuals from all sections of the society as they look forward to tax exemptions and reliefs. Even though India ranks third in purchasing power parity, only a few percentages of population pay income tax. The government's effort to widen the tax base has resulted in an 80 percent jump in number of IT returns filed. The analysis revealed that Public Revenue and macroeconomic indicators are significantly interlinked and correlated. Tax buoyancy is an indicator to measure efficiency to growth in GDP and Gross tax buoyancy coefficient remained fluctuating during the period 1990-91 to 2016-17.

Introduction
Government budget is framed in the shape of a financial plan which is a statement of income and expenditure relating to various economic and other activities that the government intends to perform in the coming period, usually a year. Budget not only shows the public receipt and expenditure but also the attitude of government towards its people, what value it given to the people and priorities given by the country for different sectors of the economy (Sonika Gupta K. S, 2016). The structure of budget frame may be different in different countries. In India, government account of the budget are presented in three parts, viz.,(a) Consolidated fund, (b) Contingency fund and (c) Public account fund. Nearly, 20 percent of public receipt comes from borrowings and other liabilities, and the corporate tax is the major source of revenue after implementing GST. Though, India is third rank in purchasing power parity (Sidhartha, 2020), only a few percentage of people pay income tax. In the last four years, the government's efforts to widen the tax base resulted in an 80 percent jump in number of IT returns filed to 6.85 crore in 2017-18 from 3.31crore in 2013-14. 2.02 crore individuals filed incometax returns in 2017-18 declaring their income, but paid zero tax since they are not in the taxable income bracket yet; the number of such companies was 3.9 lakh. So it is very essential to analyze the trend, pattern and effects of public revenue in India; more specifically, how India obtains its revenue resource for making development and the impact of such revenue on real macro variables.

Materials and Methods
The specific objectives of the paper are: (a) to analyze the trend, pattern and composition of revenue of the central government of India; and (b) to examine the impact of public revenue on major macroeconomic variables. The study is based on the secondary data collected from various

Results, Analysis and Discussion
Trend and pattern of public revenue Government raises its fund to finance their activities from various sources like tax and non-tax sources, currency mint, fees, fines, sale of public assets, and so on (Rajan Goyal, J. K, 2004). Adam Smith divided public revenue as revenue from the people and revenue from state property; while Dalton preferred to distinguish between public receipts and public revenue (Seligman, 1892).

Classification of receipts
Budget 1957-58 divided the income of central government into revenue and capital account. Revenue receipts include revenue received in the form of tax and non-tax revenue and capital account composed of market borrowing, small saving, provident fund, special deposit, recovery of loans, disinvestment receipts, and external loan (Devasia M D, Karunakaran N and Vishnu Prathap M, 2020). Table 1 shows the distribution of revenue and capital receipts. Total receipts continuously increasing since 1990, from Rs. 93951 crore in 1990-91 to 2315113 crore in 2018-19, showing nearly four-fold increase in total receipt (Fig.  1). In the first phase of 1990-99, 60.8 percent of total receipt is from revenue receipts; in the second (62 percent) and third phase (65 percent). The major reason behind this is due to the significant measures taken by government of India for augmenting revenue such as reduced tax rate and increased coverage of tax system (Odedokun, 2001; Sonika, 2016).

Components of revenue receipt
Total revenue receipts are classified into two heads; Tax revenue and Non-tax revenue. The tax revenue includes income tax, corporation tax, excise duty and custom duty. The principal sources of non-tax revenue are interest receipts, net contribution of public sector undertaking, fiscal services, general services, social and community services, economic services and external grants (Brown J. L and Howard L. R, 2002).  Table 2 shows a rapid increase in the share of tax revenue of central government from Rs. 42978 crore in 1990 to Rs. 918423 crore in 2018-19. During the first phase (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999), 73 percent of it is contributed by tax revenue, though a slight decline in its percentage is visible, the trend has maintained in the second and third phase too (Fig. 2).

Major component of capital receipts
Those receipts of central government which create liability and reduce financial asset are called capital receipts (Barry Bosworth S. M, 2008). These include loans raised by the government from public which are called market loans, borrowing by the government from Reserve Bank of India and others through sale of Treasury Bills, loans received from foreign governments and bodies and recoveries of loans granted by the central government to state and union territory governments and others (Odedokun M. O, 2001). It also included proceeds from disinvestment of government equity in public enterprises. Capital receipt also show an increase from time to time; from Rs. 38997 crore in 1990-91 to Rs. 618120 crore (Table 3). During the first phase its average share was 36 percent, increased sharply to 60.28 percent in second phase and to 77.8 percent in third phase; showing, market loans as one of the important revenue source of central government. In the case of small savings, an up-down movement, similarly, the share of other capital receipts like provident fund, special deposit, etc. has shown a decreasing at speedy rate (Fig. 3).

Major components of direct taxes
Direct tax is a type of tax where the incidence and impact of taxation fall on the same entity and tax burden can't be shifted by taxpayer to someone else. These are largely taxes on income or wealth, corporation tax, property tax, inheritance tax and gift tax. It is observed that there is a rapid increase in the share of direct taxes in total tax revenue. From 1990 to 2017, the share of personal income tax in direct tax increased (Table 4); in the first phase it was 17.78 percent, increased to 37.2 percent and 38.24 percent in second and third phase, respectively.

Trend of major components of indirect taxes
Indirect taxes are basically taxes/burden that can be shifted on to another entity or ultimate buyer, viz; sales tax (CGST/SGST), excise duty, custom duty, etc. Table 5 shows the share of Excise duty and Custom duty in total Indirect Tax. There is a rapid increase in the share of indirect taxes in total tax revenue from Rs. 36075 crore in 1990-91 to Rs. 620716 crore in 2017-18. Compared to direct taxes, the share of indirect tax has shown a speedy rise. Data also shows that the major contributors in the indirect taxes are excise duty and customs duty. After implementing GST its role is shifted to Goods and Service Tax (Brown and Howard, 2002; Sidhartha, 2020) and the share of custom duty moved downwards. In the case of excise duty, its average share has shown an up-down movement. The major reason behind this downward movement was the trade liberalization (Rajan, 2004;Barry, 2008), via, lifting trade barriers in the form of reducing the tariff and quota restrictions.

Trend of non-tax revenue
Income of the government from sources other than taxes is non-tax revenue. Non-tax revenue become payable only when services offered are availed of, viz; fine, fees, interest receipts, etc. In 1990-91 the contribution of non-tax revenue was Rs. 11976 crore and it increased to Rs. 288757 crore in 2017-18. Up to 2003-04, its contribution to non-tax revenue is greater than 50 percent but after that, it is found decreasing at a higher rate (Table 5).

Public revenue and macro variables in India
The effect of public revenue on selected macroeconomic variables and its effect on public revenue of the central government are attempted, and a two-way analysis is conducted. In the first instance public revenue is chosen as dependent variable and other variables as independent variables. In the second case, macro variable are chosen as dependent variable and public revenue as independent variables, based on the data given in Table 7.   (Table 9). The figures also reveal that the direct tax is showing more response to GDP than indirect tax.

Conclusion
Total public revenue of the central government had been continuously increasing since 1990, especially the revenue receipts. There was a rapid increase in the total receipt from Rs. 93951 crore in 1990-91 to 2315113 crore in 2018-19, registering 24 times growth. Similarly, the share of tax revenue increased from Rs. 42978 crore in 1990 to Rs. 918423 crore in 2018-19. There is a rapid increase in the share of direct taxes in total tax revenue compared to indirect taxes. This is due to the increasing share of personal income tax. The corporation tax contributed a higher share in total direct taxes with 74.98 percent; 62.21 percent and 61 percent in the first, second and third phase, respectively. The share of indirect taxes also shows a rapid increase in the total tax revenue from Rs. 36075 crore in 1990-91 to Rs. 620716 crore in 2017-18. In the first phase the average contribution on indirect taxes to total tax revenue was Rs. 56382 crore, which is greater than the average share of direct taxes in the same phase and this trend continued in the consecutive two time phases also. After implementing GST its role is shifted to Goods and Service Tax. The capital receipt shows an increase from time to time. The average amount of capital receipt in the first phase was Rs. 70256 crore and it increased to 261387 crore in second and it again increased to Rs. 507727 crore in third phase but compared to revenue receipt, the share of capital receipt decreased over time. The two-way regression analysis reveals that Public Revenue and macroeconomic indicators are significantly interlinked and correlated. Gross tax buoyancy coefficient remained fluctuating during the period 1990-91 to 2016-17. Obviously, the highest buoyancy coefficient of 2.45 was in 2002-03 and lowest in 2001-02 with a value of -0.27, indicating negative response to growth in GDP, revealed direct tax more response to GDP than indirect tax.

Source of Funding
None.

Conflict of Interest
The authors declare no conflict of interest.