Urban Household Energy Use: Analyzing Correlates of Charcoal and Firewood Consumption in Kampala City, Uganda

Charcoal and firewood contribute to greenhouse gas emissions in rural and urban areas. Although there is information about energy types used for cooking in Kampala urban environment, less is known about the correlates of charcoal and firewood consumption. This study investigated the predictors of charcoal and firewood use for cooking using the 2014 Uganda Census data set. Analysis was conducted on 41,250 households in Kampala City. Multinomial logistic regression model was fitted to predict charcoal and firewood use. Findings indicate that older household heads were more likely to use firewood than their younger counterparts. Charcoal and firewood were more likely to be used in households whose household heads were females, married and formerly married, and lived in dwelling units with two and more rooms. Conversely, chances of using charcoal and firewood decreased with the level of education, living in detached house and flat, and residing in shelter with cement screed or tile/concrete. The findings have several implications including long-term planning for improving formal education conditions, strengthening female empowerment, and upgrading dwelling conditions of the households in Kampala City.

Introduction
Charcoal and firewood dominate the sources of energy used for cooking in developing countries [1]. Over 2 billion people in developing countries are reported to rely on traditional biomass fuels including wood, agricultural residues, and dung for their daily energy needs [2]. Firewood and charcoal are the leading sources of energy for cooking in most of Uganda's households. In 2014, firewood and charcoal contributed 71.2% and 22.7%, respectively, to the sources of energy for cooking in Uganda [3]. Although the use of woodfuel (firewood and charcoal) in urban areas may be less prevalent in comparison with rural areas, the level is quite high even in Kampala City, the nation's capital and largest commercial center.
Energy demand and use have implications on various aspects of the natural and social environment [4][5][6]. Affordable and reliable energy is vital for meeting basic human needs and can be a corner-stone of development.
Having access to modern energy sources such as electricity or liquefied petroleum gas (LPG) can impact human wellbeing by reducing health and safety risks often associated with traditional energy use [7]. Modern energy sources can also translate into decreasing time budget constraints on household members, particularly women and children who usually take substantial time and effort in cooking and collecting firewood [8]. When households move up along the energy ladder and use less of woodfuel, emissions of greenhouse gases tend to decrease [9].
Broad categories of energy have been suggested. A distinction is made between traditional fuels, transition fuels, and advanced fuels [2]. Animal waste, agricultural waste, and firewood are classified as traditional fuels, while charcoal, kerosene, and coal are categorized as transition fuels. e advanced fuels are biofuels, LPG, and electricity. Kowsari and Zerriffi (2011) present a three-dimensional energy profile comprising of traditional fuels (crop residue, woodfuel), transitional fuels (charcoal, kerosene), and modern fuels (LPG, electricity). Substantial effort has been made towards understanding levels and patterns of woodfuel dynamics [10][11][12]. Woodfuel consumption levels are quite high [10][11][12][13] with consequences that pose risks to the sustainability of the resource. Interest is rising towards deeper reflection on factors with which woodfuel is associated [13][14][15][16]. Studies indicate that woodfuel is associated with age [17], education [14], and household location [18]. In the latter study, residence ownership, dwelling category, household income, and education level of household head were other factors found to be important in influencing the choice of fuel.
High level combustion of biomass including charcoal and firewood is a matter of concern for environmentalists considering that combustion is one of the major sources of greenhouse gases [19]. Effects of smoke arising from burning woodfuel are one of the predisposing factors to acute respiratory infections (ARI) in young children [20]. e reliance on biomass for cooking and heating purposes exposes many women and young children in developing countries to high levels of indoor air pollution. To manage these challenges, United Nations has continued to advocate for affordable and clean energy. Goal 7 of the Sustainable Development Goals (SDGs) aims to ensure access to affordable, reliable, sustainable, and modern energy [21].
Trends indicate higher prevalence of charcoal and firewood in rural areas in comparison with Uganda's urban areas. Although the use of charcoal and firewood happens in urban environment, the prevalence in Kampala City is substantially high in spite of rising electricity coverage [3].
ere is some evidence of links between household woodfuel consumption and energy insecurity [22], but less is known about the correlates of charcoal and firewood use [23]. Evidence is particularly limited regarding the factors associated with woodfuel use for cooking in urban households.
is paper fills the gap by analyzing predictors of charcoal and firewood use in Kampala City. e main objective of the study was to examine the correlates of urban household use of traditional energy (charcoal and firewood) for cooking in Kampala City, Uganda.

e Energy Ladder Model.
e paper was informed by previous work on the energy ladder model and the hypothesized determinants of household energy choice [7]. e energy ladder model, which was developed based on the correlation between income and uptake of non-solid sources of energy such as electricity, describes a pattern of fuel substitution as a household's economic situation changes [24]. e model ranks fuels based on efficiency, cleanliness, and convenience of storage and usage [7]. Consequently broad categories are suggested, namely, traditional fuels (animal waste, agricultural waste, and firewood), transition fuels (charcoal, kerosene, and coal), and advanced fuels (biofuels, LPG, and electricity) [2]. Traditional fuels are at the bottom of the ladder, while the advanced fuels are at the apex ( Figure 1). e model presupposes that households switch from traditional energy systems at the bottom of the ladder to modern ones higher up the ladder. e shift depends on factors such as household income, fuel and equipment costs, availability and accessibility of fuels, reliability of modern fuel distribution, and relative fuel prices [25]. e model suggests further that as families gain improved socioeconomic status, they abandon first-stage technologies that are inefficient, less costly, and more polluting. Rather, the pattern gradually shifts towards second-phase transition fuels and, ultimately, to the higher-order fuels (LPG and electricity) in the third phase. While higher-order fuels may be costly, they are considered more efficient and may require less labor and produce less pollution per unit of fuel [25].

Factors Determining Household Energy Choice.
e conceptual framework used in the paper is informed by previous work on factors determining household energy choice (Kowsari & Zerrifi, 2011). e authors posit that household energy choice depends on household decisions based on a complex interaction between endogenous (household) factors and exogenous factors (external factors). e endogenous factors are economic (such as household/ personal income, remittances, economic activity, and remittances), sociodemographic (including age, sex, marital status, and education), and behavioral (such as food taste preferences, practices, and lifestyles). e exogenous factors comprise physical environment (location and type of dwelling unit), energy policies and supply (energy affordability, availability, reliability, tariffs, and subsidies), and energy device characteristics (device conversion efficiency, payment method, and complexity of operation). ese factors are deemed to be closely interrelated and tend to operate in conjunction rather than in isolation.
In our study, the endogenous demographic factors operationalized are age, sex, and marital status, while the endogenous socioeconomic factors analyzed are education and economic activity ( Figure 2). Type of dwelling unit, housing condition, number of rooms in house, and sanitation facility are the exogenous physical factors, while energy affordability, availability, and reliability are the exogenous energy policy/supply factors. We hypothesize that the endogenous and exogenous factors influence the choice of urban household source of energy for cooking. us, this study was set out to test three hypotheses: first, charcoal is less likely to be used in households headed by persons of higher levels of education compared with their counterparts with no formal education. Second, firewood is more likely to be used in households headed by females than those headed by males, and last, charcoal is less likely to be used in households living in tenements (one-roomed shelters) than those residing in detached houses.

Source of Data and Sample Size.
is paper uses secondary data collected in the 2014 Uganda National Population and Housing Census whose census reference night was set as 27 August 2014. Data were collected from persons who spent the census night in each of the country's households. A household questionnaire was used to collect information on population, socioeconomic, housing, and household-based agricultural characteristics. is was obtained from 112 districts (including Kampala City) that constituted the country. Kampala City was selected for our study being the largest metropolis in the country and a major urban consumer of woodfuel. Household data for Kampala City (41,525 households) were consequently extracted from the main national dataset for this study.

Measurement
Variables. Source of energy for cooking was the outcome variable in our study. e census asked questions about the type of energy used for household lighting and cooking. e question that sought information on cooking was: "What source of energy does this household mainly use for cooking?". e sources of energy captured during the 2014 census were national grid electricity, solar electricity, generator electricity, thermal plant electricity, gas, kerosene, charcoal, firewood, cow dung, grass, and others. Owing to extremely low frequencies on some sources, re-coding of energy source was done during analysis to yield four major categories, namely, charcoal, firewood, electricity, and 'others.' e latter category includes gas, kerosene, grass, reeds, and cow dung. Energy for cooking, the dependent variable, was analyzed with several independent variables. ese were age, sex, marital status, level of education, economic activity, type of dwelling unit, number of rooms in dwelling unit, shelter floor material, and toilet facility.   (2011)). ree-dimensional energy profile: a conceptual framework for assessing household energy use (energy policy: Elsevier).   (2011)). ree-dimensional energy profile: a conceptual framework for assessing household energy use (energy policy: Elsevier).

Endogenous factors
logistic regression model was fitted to isolate the predictors of household charcoal and firewood use. is was done considering that the dependent variable had several response categories, namely, charcoal, firewood, electricity, and others. Relative Risk Ratios (RRR) were computed to analyze the chances of using charcoal and firewood in comparison with electricity, which was set as the base outcome. A correlation test was performed, and results point to no or minimal correlation for variables included in the model.

Ethical Issues.
As indicated earlier, our study used secondary data collected in the national census. e Uganda Bureau of Statistics (UBOS), which is the National Statistical Office (NSO) that conducts national censuses, makes public data collected in censuses and surveys. National institutions of higher learning are particularly encouraged to use these data for research and other academic purposes. We were, therefore, able to freely access the data from the NSO for our analysis. Table 1 indicates that over threequarters (77.8%) of the households in Kampala City mainly used charcoal for cooking. About three percent (2.6%) used firewood, bringing the overall usage of woodfuel (charcoal and firewood) among households to 80.4%. e percentage using electricity and other fuels was 8.2 and 11.5, respectively. Table 2 indicate that the majority of the respondents were aged 30-34 (42%) followed by those in the 15-29 age bracket (38%), while those aged 45-59 and 60-95+ were 16% and 5%, respectively. Seven in ten were males, while just 3 in 10 were females. About 6 in 10 (60%) were married, while one-quarter (25%) were never married.

Results in
Regarding education, nearly half (48%) had secondary education, while just over one-fifth (22%) had primary education. ose with tertiary and higher education were 11% and 16%, respectively. Just over half (53%) were paid employees, while slightly over a quarter (27%) were ownaccount workers. e percentage of those who were employers and unemployed was 5% and 4%, respectively. Only about a quarter had received remittances in kind and cash, and almost all were not involved in any household-based enterprise (99%). Table 2 shows other distributions by dwelling unit, floor material, number of rooms in dwelling unit, and toilet facility.

Predictors of Urban Household Energy Use.
A multinomial logistic regression model was fitted to analyze predictors of energy used for household cooking. e factors that significantly influenced the type of energy were age, sex, marital status, education, type of dwelling unit, number of rooms, and type of toilet facility (Table 3). e predictors are presented at 0.01% significance level and in reference to electricity, which is the base outcome.
In comparison with persons aged 15-29, the chances of using firewood were increased for persons aged 45-49 (RRR � 1.99) and 60+ (RRR � 4.13). Being female increased  the chances of charcoal and firewood use (RRR � 1.52 and RRR � 1.30, respectively) but reduced the likelihood of using other energy types (RRR � 0.63). Being married and formerly married increased the chances of using charcoal and firewood in comparison with being never married. e likelihood of using charcoal for those who were married and never married was RRR � 1.94 and RRR � 1.79, respectively. However, being married and never married decreased the chances of using other types of energy (RRR � 0.34 and RRR � 0.65, respectively).
In comparison with persons who did not have formal education, the chances of using charcoal reduced with increasing level of education. e chances of those with secondary, tertiary, and degree level of education were RRR � 0.71, RRR � 0.62, and RRR � 0.56, respectively.
Similarly, the likelihood of using firewood reduced with education. In comparison with those who did not have formal education, the chances were lower for those with primary, secondary, tertiary, and degree level of education (RRR � 0.97, RRR � 0.37, RRR � 0.29, and RRR � 0.16, respectively). However, education was associated with increased chances of using other energy sources for persons of degree level of education (RRR � 2.25).
Compared to living in tenement, residing in detached/ semi-detached house, flat, and other type of dwelling unit reduced the chances of using charcoal (RRR � 0.45, RRR � 0.14, and RRR � 0.19, respectively). Only living in flat reduced the chances of using firewood. e chances of using other types of energy were also reduced by living in detached, flat, and other types of dwelling units. e number of rooms in a dwelling unit also influenced energy use. Living in two rooms and three rooms and above increased the chances of using charcoal (RRR � 1.32 and RRR � 1.52, respectively). Table 3 further indicates that living in shelter with title or concrete floor reduced the chances of using charcoal and firewood (RRR � 0.69 and RRR � 0.21, respectively) compared with living in shelter with rammed earth floor. Residing in shelter with cement screed and other floor material also reduced the chances of using firewood (RRR � 0.32 for both categories). However, living in shelter with tile or concrete floor increased the chances for using other types of energy. In comparison with shared toilet facility, having a not shared toilet facility reduced the chances of using charcoal (RRR � 0.80) but increased the likelihood of using other types of energy (RRR � 1.23).

Discussion
e main objective of the study was to examine urban household energy use for cooking in Kampala City, while the specific objective was to analyze the correlates of charcoal and firewood consumption. Findings indicate that the likelihood of firewood use increased with age. e trend/ pattern may relate to the tendency for some older persons to maintain the practice of using traditional fuels as relatively younger persons switch to more modern forms of energy. Other studies have shown that older persons restrain to move away from their current practices [2]. Age has similarly been found to be one of the socioeconomic factors that promote popularity and use of woodfuel [17].
As hypothesized, charcoal was less likely to be used in households headed by persons of higher levels of education compared with their counterparts who did not have formal education. It is likely that as education increases, the probability of access to resources improves.
is could enable households to afford cleaner energy sources. Households headed by persons of higher education may also have had less time to spend on chores, probably due to engagement in the formal sector; hence, the use of less time-consuming energy sources. Conversely, those with no formal education may have engaged more with charcoal use probably owing to their relative low affordability of cleaner sources. Education has similarly been found to be a strong predictor of cooking fuel choice in India's states of Kerala and Rajasthan [14] and wood consumption in Brazil's Atlantic Forest Area [26].
Female-headed households were more likely to use firewood for cooking (as hypothesized) than their male-headed counterparts. e higher likelihood is perhaps expected considering that, in the Ugandan setting, a large proportion of females are disproportionately under-resourced in comparison with their male counterparts. e disadvantage could have translated into limited household resources for affording cleaner sources of energy. Studies elsewhere have similarly indicated that a large proportion of female-headed households tend to have compromised ability to access modern fuels, resorting to the comparatively less costly woodfuel sources [27]. e higher likelihood of using charcoal and firewood among the currently married and ever married persons could be explicable in terms of differences in domestic roles and responsibilities. For example, while the married could have headed sizable households with greater demand on food preparation, some of the never married persons may, conversely, have headed smaller households where cooking demands were less, and, hence, it was possible to use alternative and more convenient forms of energy such as electricity and gas.
Economic activity influenced energy use with the unemployed having more chances of using woodfuel. Conversely, households in which there was some household enterprise had reduced chances of using woodfuel. Variations in financial status and capacity to afford cleaner energy sources may have explained the disparity. is finding appears to dovetail with the energy ladder hypothesis, which asserts that as income rises, energy choices shift towards less traditional fuels [7]. Income level has been reported to be a major factor influencing household energy utilization and changing behaviors in Western Kenya [28].
Contrary to our hypothesis, charcoal was more likely to be used for cooking in households living in tenements (oneroomed shelters) than those residing in detached houses. It may be the case that those living in tenements were of lower socioeconomic status and, hence, less able to afford cleaner energy sources than those residing in detached houses. Other plausible explanation relates to the link between dwelling unit and house occupancy tenure. In the setting of Uganda, particularly Kampala City, dwelling unit and house occupancy tenure tend to be associated, with those living in detached houses likely to be owner occupiers, while those in tenements likely to be renters. Some renters could be handicapped in the event that the landlord did not make necessary provisions for accessing cleaner sources of energy such as electricity. Studies on relationship between house occupancy tenure and household energy use [29] have indicated that being the owner of a house does not necessarily imply having higher purchasing power but could mean prevalence of freedom of space management in the house. Tenants may have less choice as they are expected to comply with occupancy contracts and regulations which may compromise their leeway in deciding energy options.
In comparison with living in houses with rammed earth floor, living in shelters with cement screed and tile/concrete floor was associated with lower chances of using woodfuel. is finding suggests that the quality of housing influences energy use. Results further show that the likelihood of using charcoal increased with the number of rooms in house. is could be related to space availability for storage and use of charcoal. is dovetails with studies elsewhere, which indicate that house size, measured by the number of rooms, is associated with traditional and transitional energy use [2]. Findings also indicate that households whose toilet facilities were not shared were more likely to use charcoal than those for whom it was shared. Overall, it is probable that the underlying factor for differentials in charcoal use by housing quality, space, and nature of toilet facility is the economic factor.

Conclusion and Implications
Kampala City households are at the lower end of the energy ladder characterized by charcoal and firewood as the leading sources of energy for cooking. Education influences household energy use with households headed by persons of higher levels of education being more likely to use charcoal than their counterparts with no formal education. e sex of the household head influences energy use with female-headed households being more likely to use firewood than their maleheaded counterparts. e type of dwelling unit is a correlate of energy use with households living in tenements more likely to use charcoal than their counterparts residing in detached houses. e findings have several implications including longterm planning for improving formal education conditions, strengthening female empowerment, and upgrading dwelling conditions of the households in Kampala City.

Limitations.
e 2014 National Census Dataset used for this study lacked key variables, which would have enriched the study. Consequently, we were unable to analyze wealth index and the effect of the endogenous factors, namely, energy policy and supply, as well as behavioral and cultural characteristics such as food preferences and taste, practices, lifestyles, and cultural diversity. Similarly, we were unable to analyze the role of energy device characteristics such as conversion efficiency, cost, payment method, and complexity of operation.

Data Availability
e data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest
e authors declare that there are no conflicts of interest regarding the publication of this paper.


extfieldsofstudy:
Physics
Computer Science

text:
Research on the Evaluation of Construction Regional Development Level Based on the Whole Industry Chain

From the perspective of the whole industry chain, this article divides the construction industry from the construction process, construction products, and participants into the whole process, all-round, and full-staff principles, and divides the construction industry of each province into upstream industries and midstream industries based on all links of the entire industry chain. This paper constructs the evaluation index system of three dimensions of downstream industry. Based on valid statistical data, the development level of the construction industry in 28 provinces in my country in 2019 was analyzed based on combination weighting and principal component analysis, and the development level of the construction industry in each region was ranked. The results show that there are obvious regional differences in the development of construction regions. The development level of the construction industry in the eastern and coastal provinces and cities is significantly better than that in the western and central provinces and cities, which is consistent with the status and verifies the validity of the model.

Introduction
The "full industry chain" model of R&D, manufacturing, marketing, and service has a strong theoretical logic for weakening the dilemma of "low-end lock-in", which is embodied as an "industrial community" at the micro level of the enterprise [1] . The "full industry chain" model of R&D, manufacturing, marketing, and service has a strong theoretical logic for weakening the "low-end lockin" dilemma. A mature domestic industry chain should have its own ecosystem, including upstream and downstream related companies and related industries , Embodied as a kind of "industrial community" at the micro level of the enterprise [2] . In this paper, through the construction of the whole industrial chain, effective management and control of each link of the industrial chain is of great significance for the realization of effective and accurate supply and traceability of building product quality and safety, and the improvement of construction efficiency and quality [3] . Carrying out regional evaluations based on the entire construction industry chain and improving the overall synergy of the construction industry chain have important guiding and practical significance for promoting the development of my country's construction industry.
The foreign discourse on the industrial chain can be traced back to Adam Smith's division of labor in "The Wealth of Nations" [4] . In recent years, literature research has mainly focused on production chain, value chain and supply chain [5]- [8] . Many domestic scholars have carried out research from different perspectives on the subject relationship and performance management of architecture [9]- [11] . In recent years, many scholars have conducted research on the development of architectural regions. Among them, Zhao Likun [12] (2019) analyzed and evaluated the prefabricated construction industry in 7 regions of China, including North China, East China, and South China; Zhang Miao [13] (2018) Constructed a comprehensive evaluation index system for the sustainable development of the construction industry in the new region; Liang Hao [14] (2017) studied the classification of construction industry enterprises ; Xu Xinwei [15] (2017) constructed an evaluation index system for the level of residential industrialization . The research article found that the development level of my country's construction industry is significantly different and the evaluation index system based on the construction of the entire industry chain has not been established. Most of these studies take a certain area as the overall research object, or the evaluation method is relatively simple, and the evaluation of each link of the industry chain has not been evaluated. In-depth study. By constructing a wholeindustry chain building evaluation system, this paper uses the combined weight method and principal component analysis method to evaluate the development level of building industrialization in 28 provinces and cities in my country for each link in the industry chain.

Analysis of the whole construction industry chain structure
The industrial chain is the material basis of the supply chain. The industrial chain is expressed through the products of enterprises and enterprises. The industrial chain is a material basis of the supply chain, that is, the industrial chain has two nodes, the enterprise and the product, while the supply chain has only one node for the enterprise , With the extension of the roundabout production industry chain, the division of labor and cooperation in the construction industry has developed from not only coordinated within the enterprise to more extended to enterprises.The whole construction industry chain consists of three industries: upstream industry, midstream industry and downstream industry. The government is in a core leading position in the entire construction industry operation process. The basic structure of the entire construction industry chain is shown in Figure 1: First, the design institute conducts the modeling; then, the building material supplier provides the raw materials, and the prefabricated component processing plant inputs the raw materials to massproduce the prefabricated components according to the component model provided by the design institute. The logistics company is entrusted to produce the prefabricated components according to the on-site construction schedule and the prefabricated component factory The component progress is carried out for the transportation of prefabricated components. The construction company completes the construction of the building project through a highly mechanized construction process, and then  With the rapid development of the construction industry chain, research from a qualitative perspective can no longer meet the actual needs. The analysis of key nodes in the industry chain should be quantified and evaluated through numerical analysis. This article constructs the entire construction industry chain, and analyzes the key node enterprises in the above-mentioned representative key links in the entire industry chain. Including the government, developers, consulting and design companies, building materials companies, component factories, construction companies, supervision companies, property companies, building demolition recycling companies and other 10 node companies. Figure 2 shows the participants and information exchange of the whole construction industry chain.

Evaluation System
The industrialization of construction requires a systematic thinking of the entire industry chain, from R&D, design, component production, part production to construction, installation, and delivery and operation. The collaborative work of enterprises in the industry chain is integrated, and the building is regarded as a complete product and is being constructed. In the early stage, issues such as maintenance and renovation during the use of the building were considered. This paper constructs an evaluation index system for the entire construction industry chain according to the key node enterprises in the entire construction industry chain and their impact factors, as shown in Table 1.

Evaluation method
At present, traditional evaluation methods are usually used for the evaluation of building areas, such as analytic hierarchy process, fuzzy evaluation analysis method, entropy weight method, etc [11]- [17] . The evaluation results of these traditional evaluation methods depend on the scientificity and effectiveness of the established evaluation index system. However, in practical applications, due to artificial design reasons, there are often strong correlations between different indexes, causing the same effect to be counted multiple times, The final evaluation result is difficult to achieve satisfactory reliability. Combination weighting method is a comprehensive weighting method that integrates subjective weighting and objective weighting. It not only compensates for the deviation of the weight value caused by a single weighting method, but also takes into account the preferences and recognition of decision makers (experts). Know the limitations.

Evaluation system application
This paper selects 28 provinces and cities in my country as the research objects to evaluate the development level of the entire industry chain of the construction area, using a total of 12 indicators in the upper, middle and lower reaches of the construction industry chain in each province in 2018 as the basic data of the evaluation indicators, and unified through the evaluation matrix For processing, the evaluation matrix must be normalized. Through subjective weight and objective weight analysis, the combined weight of each indicator is obtained. If the sum of the weight of each indicator is greater than 0.85, these indicators can be considered effective. Finally, the comprehensive evaluation of entropy method and principal component analysis results in the scoring table of each province as shown in Figure 3. Judging from the calculation results, the government's guidance to the construction industry and technical standards and regulations have a role in promoting the development of regional buildings. From the perspective of regional rankings, the development of China's regional construction industry is currently unbalanced, showing obvious geographical distribution from strong to weak. From the eastern coastal areas to the central and western regions, there is a stepped distribution from good to bad, and the north-south development is unevenly distributed.  Figure 3. Distribution map of scores across provinces across the country

Conclusions and Outlook
The eastern coastal area has developed economy, the construction industry started early, the construction industry structure is stable, and the construction industry chain is relatively complete; the construction industry market in the northeast and some central and western provinces started late, and the industrial structure is or needs to be adjusted, but its market demand is relatively high. It is an important area for the development of the construction industry. The report of the 19th National Congress of the Communist Party of China clearly pointed out that the implementation of the regional coordinated development strategy, the extremely unbalanced development of my country's regional construction industry and the accelerating increase in the development of the construction industry in the east and west have been pointed out and are being quickly stopped. As far as the construction industry is concerned, backward regions should give full play to their local advantages, integrate resources, adjust the local industrial structure, improve the market system, and develop faster regions should innovate to lead and optimize the development of the regional construction industry, and establish a more effective new mechanism for regional coordinated development. Only by opening up and perfecting the industrial chain can the adjustment of the industrial structure promote the upgrading of traditional industries, transform the construction mode, and comprehensively improve the quality of construction, and promote the better development of my country's construction industry.


Accumulating Risk Capital Through Investing in Cooperation

Recent work on promoting cooperation in multi-agent learning has resulted in many methods which successfully promote cooperation at the cost of becoming more vulnerable to exploitation by malicious actors. We show that this is an unavoidable trade-off and propose an objective which balances these concerns, promoting both safety and long-term cooperation. Moreover, the trade-off between safety and cooperation is not severe, and you can receive exponentially large returns through cooperation from a small amount of risk. We study both an exact solution method and propose a method for training policies that targets this objective, Accumulating Risk Capital Through Investing in Cooperation (ARCTIC), and evaluate them in iterated Prisoner's Dilemma and Stag Hunt.

INTRODUCTION
Sequential social dilemmas (SSDs) are games where short-term individual incentives conflict with the long-term social good. Such games are pervasive and describe situations in which we would deploy automated multi-agent systems. In many of these situations, we only control one of the agents involved, as is the case with self-driving cars where the other agent could be a human or a self-driving car from another company. Many methods have been proposed for training policies that would better optimize social good [5,12,13,15]. However, many of these methods do so at a greater risk of being exploited. A policy that optimizes for social welfare could allow other policies to be selfish without long-term consequences. So the welfare-maximizing policy may fare worse in these settings than if it had only optimized for its own selfinterest. To some extent, this is unavoidable since every choice to cooperate leaves room to be exploited. However, though this trade-off is inevitable, it is not as stark as it first appears.
To study this trade-off formally, we present two objectives. The first objective is the well-studied notion of -safety. Previous work shows that -safe policies in sequential settings risk what the policy won in expectation [6], allowing them in the long run to take much larger risks. The second objective is to perform well with cooperation-promoting policies. We define cooperation-promoting policies to be such that they are more cooperative when faced with cooperative policies. Since perfect safety would require only defection and perfect performance with cooperation-promoting policies would require only cooperation, these two objectives are in clear conflict.
Throughout this work, we find it convenient to conceive ofsafety using the concept of risk capital. Risk capital is the amount of capital an investor is willing to lose. In our case, we use risk capital to refer to the amount of utility we are willing to risk, which for -safe policies is . In this framing, we can think of prior work, showing that -safe policies risk what the policy won in expectation, as noticing that a policy does not need more risk capital for reinvesting their unexpected winnings. Since cooperation could always be met with defection, cooperating necessitates some amount of willingness to lose utility. However, like an investment, cooperation often results in returning more utility than was risked. This returned utility can then be reinvested without risking worse outcomes, leading to growing risk capital over time, thus more cooperation over time even for a small initial amount of risk capital.
This argument shows an interesting fact about the trade-off between safety and cooperation -that though cooperation with total safety is impossible, giving away even a small amount of safety will lead to nearly optimal cooperation in the long-term. We formalize this argument as a trade-off between safe beliefs and cooperation promoting beliefs. We propose a method to train policies that satisfy this objective, which we call Accumulating Risk Capital Through Investing in Cooperation (ARCTIC), based on the idea of investing risk capital to achieve long-term cooperation. We test this method in the simple domains of prisoners dilemma and stag hunt. However, we expect investing in cooperation to be a broadly applicable principle for developing safe and cooperative agents. To achieve full safety, we begin with fully safe beliefs and adapt the safety levels according to opponent play. A positive initial risk capital can be used with minimal effect on long-term safety while achieving cooperation more easily.

Literature Review
The problem of cooperation in sequential settings has long been studied in game theory. One of the most famous strategies is Rapport's tit-for-tat [20], which achieved great success in Axelrod's tournament [1]. In part, Axelrod attributed it's success to its ability to promote cooperation and is not exploitable after the first move.
Human experiments of sequential social dilemmas often find cooperation as the most popular strategy. Gardner et al. [7] use bounded rationality to explain the high levels of cooperation in lab experiments where participants played a common pool resource game and could communicate with each other. Human cooperation seen in social dilemmas is often founded in indirect reciprocity [19]. One such suggestion, translucent game theory [2], explains cooperative behaviors through the idea that others' could detect an intended defection and punish it, which serves as the inspiration for our use of policy-conditioned beliefs. These transparency considerations, such as the ability to view opponent facial expressions, change behaviors in social dilemmas [3,11].
The social dilemmas we consider are Prisoner's Dilemma and Stag Hunt. In Prisoner's Dilemma, Press and Dyson [18] showed that there exist evolutionary dominant strategies that can only be outperformed by players who have a theory of mind about their opponent; and, when combining strategies with a memory of one and theory of mind, stable strategies have been classified [10]. For Stag Hunt, the effects of network topology are important for the emergence of cooperation in games played on a network [21].
In the reinforcement learning (RL) literature, temporal difference learning has been applied successfully to learn cooperation in Prisoner's Dilemma since it maximizes future rewards [16]. Leibo et al. [14] extended classic sequential social dilemma examples into the domain of deep reinforcement learning. Following on from this work, Jaques et al. [13] used social influence as intrinsic motivation to achieve coordination and communication between agents in SSDs. Additionally, approximate Markov tit-for-tat [15] maintains desirable properties from tit-for-tat but applied to deep learning in general games. Eccles et al. achieved reciprocity through a system of innovators who maximize their own rewards and imitators who learn to mimic their behaviors by measuring the niceness of actions [4,5], resulting in good performance in SSDs.
Safe strategies are essential to restrict adversarial opponents [9]. Ganzfried and Sandholm [6] explore the necessary properties for a policy to allow for safe opponent exploitation. Yet, there is a gap in the literature for safety in SSDs. Most research is concerned with adapting tit-for-tat like mechanisms to handle adversaries, whereas our proposed algorithm guarantees the safety property.

Paper Outline
We begin in Section 2 by outlining the necessary mathematical preliminaries. In Section 3, we establish the conditions for thesafety of beliefs. Then we define a policy-conditioned belief that incentivizes cooperation in SSDs in Section 4. In Section 5, where we show that the trade-off between safety and cooperativeness decreases with length of the interaction. In Section 6, we equip this cooperation inducing belief with the safety property and propose the ARCTIC algorithm. Additionally, we show ARCTIC has desirable properties in matrix SSDs: Prisoner's Dilemma and Stag Hunt. Finally, we prove its success at defending against adversaries whilst maintaining cooperation in RL agents in Section 7. . A Nash equilibrium is a strategy profile such that ∈ ( − ) for all ∈ . The Risk What You've Won in Expectation algorithm [6] plays an -safe best response to a model of an opponent's strategy and achieves safety.

MATHEMATICAL PRELIMINARIES
Social dilemmas are games in which agents can either cooperate or defect where joint cooperation gains the highest total rewards but there is an incentive to deviate from this. We will explore the conditions for beliefs in a sequential social dilemma (SSD) to incentivize cooperation. The possible payoffs of each stage games are reward for mutual cooperation, punishment for mutual defection, sucker if exploited, and temptation for exploiting. Table 1 shows the form of a two-player matrix game to be social dilemma, such as Prisoner's Dilemma and Stag Hunt.
Reinforcement learning (RL) problems are those which comprise an agent learning how to behave in an environment where they receive rewards for their actions. The environment is represented by a state variable, ∈ , and the principle task of the agent is to estimate the value of choosing an action, ∈ , given the current state. The goal is to then find an optimal policy stating the action to be taken in a given state to achieve the highest rewards. In multi-agent RL, multiple agents act in the same environment.
A Markov game is a tuple ( , , ( ), , ( ), ) for ∈ : set of agents where | | > 1, state space , action spaces with joint action space := 1 × ... × | | , Markovian transition model : × × ↦ → [0, 1], reward function : × × ↦ → R and a discount factor ∈ [0, 1). The goal of an agent is to select a sequence of actions, or policy , to maximize the cumulative discounted return = ∞ =0 + +1 . Such a sequence of actions is called the optimal policy * . The joint policy : → Δ( ) is defined as ( | ) := ∈ ( | ). The state-value function for agent , or value function, : → R describes the expected value of following policy when your opponent follows policy from state : where − represents all other agents in .

SAFE BELIEFS
As a mathematical convenience to represent both our safety criteria and our beliefs about the distribution of opponent strategies, we introduce policy-conditioned beliefs. A policy-conditioned belief is a function : Σ → Σ − . Note that these beliefs are only a mathematical convenience and should not be thought of as a literal reflection of our setting, since players move simultaneously, the strategy of player does not causally affect the other players' strategies on the same time step. However, policy-conditioned beliefs allow us to represent both the idea that our opponent is drawn from some fixed distribution and the idea that our opponent might be adversarial, in a single formalism. The set of best responses to a policy-conditioned belief is defined as A policy-conditioned belief is -safe if, and only if, for all ∈ ( ), is -safe. An example of a safe belief is the adversarial policy-conditioned belief, we define this policy-conditioned belief as The safety property is desirable to minimize risk in environments where an adversarial opponent can exploit a policy. However, playing a safe strategy may end up with suboptimal outcomes, such as in Prisoner's Dilemma and Stag Hunt. Thus, we shall classify -safe strategies that can safely cooperate with other agents in any multi-agent games. The payoff matrix for players is assumed to be normalized on [0, 1] for the purpose of simplicity; otherwise, there is an additional coefficient of the range of payoffs 1 . Proposition 1. For any two-player game with a Nash equilibrium * , ∀ ∈ [0, 1] and ∀ ∈ , ∃ ∈ Σ such that || − * || ∞ ≤ and is -safe 2 .
Proof. Since we assumed the payoffs are normalized, we have Now that we have found conditions for the existence of -safe strategies, we will prove similar properties for policy-conditioned beliefs. Define a policy-conditioned belief to be -close to a policyconditioned belief ′ if, and only if, Proof. For all ∈ Σ and any policy-conditioned belief such that is -close to , can be bounded by where Since the utility function is convex we can find an upper bound: Rearrange the difference in expected utility of and : Therefore, the inequality holds true. □ Thus, beliefs close to have similar expected utilities. Now we can address the safety property in the same context. Proposition 3. If the policy-conditioned belief is -close to the adversarial policy-conditioned belief , and utilities are bounded on [0, 1] then is -safe.
Proof. Since is -close to the adversarial policy-conditioned belief , then by Proposition 2, ∀ ∈ Σ we have Thus, − ≤ [ ( , ( ))]. Hence, ∀ ∈ Σ , is -safe. This is -safe. □ Proposition 3 shows that we can append any policy-conditioned belief with some level of an adversarial policy-conditioned belief and it will be -safe for some . Consequently, we can take a policyconditioned belief that naively cooperates with any opponent and bound its safety through creating an uncertainty; either this belief is true or they face an adversarial opponent.

COOPERATION INDUCING BELIEFS
In the previous section, we proved that we could take a cooperation inducing policy-conditioned belief and still maintain the safety property through uncertainty about whether the opponent faced is, in fact, an adversary. Now we must develop a policy-conditioned belief that we know will incentivize cooperative behavior in sequential social dilemmas. First, we consider two-player matrix SSD games.
Let the strategy of player be ( ,¯) where ∈ [0, 1] is the intended probability of cooperating in the next round and¯∈ [0, 1] is the probability of cooperating for all subsequent rounds. This format is chosen for simplicity. Similarly, the current and future strategies of their opponent are denoted by ( , 1 − ,¯, 1 −¯). For discounted future returns, define the expected returns : where ∈ (0, 1] is the discount factor. A policy-conditioned belief in the sequential game is a function : Although opponent strategies are unseen, suppose that a player believes that their chosen strategy for the next round will change the strategy of their opponent for all subsequent rounds depending on their level of cooperation. For some ∈ (0, 1], if player chooses to cooperate with at least proportion then their opponent's future cooperation level will not decrease, and for cooperation less than , their opponent's level of cooperation will not increase for future rounds. Let player such a policy-conditioned belief , where stands for cooperation-promoting, formally defined as for some ∈ (0, 1] where ≤ + and ≥ − . This belief is similar to a belief the opponent plays tit-for-tat with the additional condition that the opponent can view intended mixed strategies and is willing to invest or reject risk capital in the long-term. Let us find the necessary conditions on for cooperation to be an equilibrium strategy against similar agents, and hence, be a cooperation inducing policy-conditioned belief as required. For cooperation to occur, we require that defection is not a best-response, i.e. ∀ > 0, (( ,¯), ( )) ≥ ((0,¯), (0, 1)). ] is increasing in . As such, full cooperation will be dominant, so will play = 1. Otherwise, they will play = , in which case should be set to 1 to induce fully cooperative behavior. + − − is the change in cooperation that the next strategy will induce in the opponent's future strategy.
These beliefs can be summarized as those who believe their next action will affect opponents' future strategies such that they prefer to cooperate now to avoid a reduction in future utility. We call a strategy that follows this system . Other beliefs from the literature that are cooperation promoting could also be substituted here, such as translucency [2].

TRADE-OFF BETWEEN COOPERATION AND SAFETY
In the previous sections, we described how to use policy-conditioned beliefs to promote both safety and cooperation. In this section, we will use these ideas to show how the two concepts are necessarily in tension but how this tension disappears in the long-run. To make this concrete, we will define the value of cooperation to be the value expected against the policy-conditioned belief , that is: Similarly, we can think of the value of safety to be the value expected against the policy-conditioned belief , that is: We will define = max { ( )} to be the optimal cooperative value and = max { ( )} to be the optimal safe value. For the sake of simplicity, we will assume that = − = 0 and = 1, which would be the case for cooperation-promoting policies when defection is a safe strategy and cooperation is socially optimal.
When we focus on the decision made in the first round, the trade-off is fairly stark. The more likely you are to cooperate, the more likely you are to be exploited. However, this also increases the likelihood of encouraging your opponent to cooperate in the following round(s). In the second round, the story is largely the same. Since we assume = − = 0, the cooperation-promoting policy would defect on the first step. Thus, the safety that your policy loses over the first two steps is proportional to the probability that you cooperate, and the amount that you lose against the cooperation-promoting policy over the first two steps for defecting is proportional to the probability that you defect.
However, when we look at the third step, the trade-off is less severe. If cooperated with proportion on the first time step and is high enough that the cooperation-promoting policy cooperates with them on the second time step, it increases the budget of safety for , now being able to cooperate at + [ ( , − )] − without exceeding the safety budget. If the policy were any more cooperative, the adversary would be motivated to cooperate on the second round to be able to exploit in future rounds.
This pattern continues until it is safe for our policy to cooperate deterministically. At which point, our policy will continue to cooperate for the rest of the round. This means that the tradeoff between safety and performance with cooperation-promoting policies can be characterized by the probability of not cooperating early on, which over the long run is a negligible fraction of overall performance. This is captured formally in Proposition 5.
Proposition 5. Suppose that defection is a safe strategy and cooperation is socially optimal. Let be -safe. Let be the probability cooperates on round against a cooperation-promoting belief and assume [ ] ≤ −1 + for some constant > 0, as is the case when = − = 0. Then: , notated as such because Φ 1 is the golden ratio, and = min{⌈− log Φ ( )⌉, }. Moreover, when [ ] = −1 + , there is an -safe policy which makes this bound tight.
Proof. Let be -safe so = − ( ). The result follows from the fact that cooperation at each round gives a bound for safety, which we define to be˜as follows: If we take these˜to inductively define a policy , then when [ ] = −1 + , can be proven achieve the desired equality by changing all of the inequalities in the following proof to equalities. By substitution we have: We can then show by induction that˜≤ Φ . In the base casẽ 0 = = Φ 0 and in the inductive case: Where the last inequality follows from the fact that Φ + = Φ 2 .
This inequality and ≤ 1, allow us to bound − ( ) by simply summing the expected rewards: Since the socially optimal behavior is deterministic cooperation, the difference from optimal behavior only occurs when Φ < 1 which happens when < − log Φ ( ). If − log Φ ( ) is bigger than , the second term of the min would never occur in the summation. Thus, the point where the summation starts using the second term is = min{⌈− log Φ ( )⌉, }. We plug this into the final inequality and use the geometric series formula to get: We multiply both sides by −1 and add for the desired result. □ It is important to note that this trade-off, after a certain point, does not grow with . Moreover, the return on cooperation value for small reductions in the optimal safety value grows very quickly, as the small loss in safety can effectively be reinvested at each successive time step as the policy receives the gains from cooperation. Thus, in long iterated games, the optimal policy for this objective is nearly-optimal for the designer to deploy into either fully adversarial settings or fully cooperative settings. In the next section, we take this core insight as the motivation for an algorithm that uses the ideas of reinvesting this risk capital in order to achieve high degrees of both safety and cooperation.

ACCUMULATING RISK CAPITAL THROUGH INVESTING IN COOPERATION
In SSDs, if an opponent is cooperative, surplus payoff above the value of the game can be reinvested for higher long-term gain. However, we also need to be able to invest safely to avoid exploitation.
To begin with, we give agents an adversarial policy-conditioned belief to avoid exploitation and maintain safety 3 . For any surplus capital gained, , we can deviate our safe beliefs to be -safe and begin to reinvest to build trust with opponents. One such belief is := (1 − ) + , where the sequential adversarial belief is and , + , − satisfy the conditions in Proposition 4. By Proposition 3, is -safe. Again, consider the two-player matrix SSDs of the form in Table  1. If ( ) = (0, 1, 0, 1), which is true for both Stag Hunt and Prisoner's Dilemma, ∀ ∈ Σ we can write the belief as The condition for cooperation to occur is now: (1) Naturally, for = 1 this always holds. Ideally, we want to choose , , + , − such that this holds for the smallest possible so that we need to least amount of risk to allow cooperation to be a best response to these beliefs. Pseudocode for the Accumulating Risk Capital Through Investing in Cooperation is given in Algorithm 1.
Following from results in [6], this algorithm is safe. We can think of as representing the amount of risk capital we are willing to invest in an opponent. For safe play in a sequential game, we begin with no risk capital and play the minimax strategy Against the adversarial strategy, ARCTIC does not learn to cooperate as there is not enough risk capital gained from their interactions. When two ARCTIC players interact, the risk capital slowly builds over time for all as they both play cautiously.
plays from , − plays − from unknown − ; ← min( + [ ( , − )] − , 1) end since we assume our opponent is adversarial. As the game goes on, the amount of risk capital will increase against non-adversarial opponents and we can safely invest such risk capital with the expectation of a return on the investment. Gradually, we build trust against similar opponents that reciprocate collaborative behaviors.
If our opponent has been cooperating in the past, then there is enough risk capital for ARCTIC to cooperate with such an opponent. However, if they then defect the amount of risk capital drops and they are more likely to defect in the next round, similar to how a tit-for-tat strategy punishes defections. If the risk capital is high enough, then punishment of a defection is less common since ARCTIC has learned to trust the good behavior of its opponent. This mechanism stops the strategy from being exploited against adversaries whilst maximizing cooperation with allies.

Matrix Games
To test the algorithm's performance, we simulated an ARCTIC agent for 100 rounds of Prisoner's Dilemma and Stag Hunt where its opponent either followed a simple strategy or best responded to their policy-conditioned beliefs. The simple strategies played against against were tit-for-tat (T4T) and pure defector (Adv), and the policy-conditioned belief opponents followed either ARCTIC Figure 2: Simulations of 100 rounds of Stag Hunt whilst playing the ARCTIC strategy against 5 different opponent strategies. For cooperation to occur here, we need and to be large enough to satisfy condition 1.
or the cooperation inducing belief . Figures 1 and 2 show the cooperation levels and risk capital for these experiments. Experiment parameters were: random action noise 5%,¯= , and the discount factor = 0.9. Results were then averaged over 200 runs.
In Prisoner's Dilemma (Figure 1), the ARCTIC agent quickly learns to cooperate at rate against the cooperation incentivized T4T and players. When playing itself, the amount of risk capital, , increases more gradually since they are learning to trust more cautiously than a T4T or player. Against the adversarial player, the cooperation levels a very low, and thus the ARCTIC agent maintains the safety property.
Similarly, in Stag Hunt ( Figure 2) the ARCTIC agent learns to cooperate with all but the adversary. For and large enough to satisfy condition (1), ARCTIC learns to fully cooperate by the end of the 100 rounds of play. Cooperation is much easier to achieve in Stag Hunt than in Prisoner's Dilemma since defect is not a dominant strategy. Against the adversary, not enough risk capital is collected for the best response to be cooperation which preserves the safety of the strategy.
Here, we have consider ARCTIC for payoffs normalized on [0, 1] as seen in Table 1. For games where payoffs have a range greater

MULTI-AGENT REINFORCEMENT LEARNING
SSDs are often subgames of more complex multiplayer games. These types of games are of particular interest in multi-agent RL due to the difficulty of learning cooperative policies. In more complex games where teamwork is required, it is only sequences of actions that create cooperative or selfish behaviors. Thus, we consider long-term behaviors in order to capture the nature of the agents. We assume that the minimax value, , of the game can be determined. An opponent can therefore detect whether the agent is cooperating by measuring whether their rewards are at least the value of the game. The level of cooperation is now : Let player 's cooperation inducing policy-conditioned belief , be defined as where , + ( ) > , − ( ) for some threshold ∈ (0, 1]. If cooperation level is above a certain level, then the opponent will behave cooperatively. Otherwise, they will act in their own self-interest. To train agents with these cooperative beliefs, we can adapt the reward functions of their opponents as such: To train an agent with a policy-conditioned belief, it can be trained in an environment where those beliefs are true and transferred into the standard environment for deployment. We trained distributed asynchronous advantage actor-critic (A3C) [17] agents on Prisoner's Dilemma and Stag Hunt environments. Agent policies were trained with the policy-conditioned beliefs and ARCTIC where = 0.5 and¯= . Agents without beliefs were the Baseline agent (unmodified A3C) and the Adv agent. The neural network consists of two fully connected layers of size 32 and a Long Short Term Memory (LSTM) recurrent layer [8]. The learning rate for Baseline, Adv, and agents was 0.001. For ARCTIC, the learning rates were 0.00007 and 0.0001 for Prisoner's Dilemma and Stag Hunt respectively. The entropy coefficient was 0.01. The state space for the ARCTIC agents was a onehot encoded value. Each agent was trained on 3 different random seeds and results are average across these policies for 300 rollouts.

Prisoner's Dilemma
The difficulty of playing Prisoner's Dilemma with a generic multiagent RL algorithm is that defection is a strictly dominant strategy and, thus, usually converge to defecting. This means that a mechanism for agents to cooperate must be used to promote cooperation, which leaves them open to exploitation. By using ARCTIC here, the agent still acts optimally, but it acts optimally with respect to their policy-conditioned belief. Table 2 shows the cumulative rewards for trained agents after 100 rounds of playing Prisoner's Dilemma. The baseline agent performs poorly against itself due to its inability to cooperate whereas the ARCTIC agent cooperates with itself some of the time and achieves a more socially optimal outcome. With the player, ARCTIC cooperates at higher levels than with itself. Against the adversary, ARCTIC achieves close to the value of the game on average. To try and improve the level of cooperation with itself, the initial level of risk capital can be increased to improve the cooperativeness with only increased risk in the first round of play. From Table 3, we see that this leads to a more socially optimal outcome when played against itself without risking much against defectors.

Stag Hunt
For an RL agent playing Stag Hunt, multi-agent RL algorithms learn to cooperate more effectively than Prisoner's Dilemma but this leaves them exploitable to adversaries.
In Table 4 the scores for agents in the Stag Hunt tournament can be found. Here, the baseline agent performs well against themself but achieve a poor score against adversaries. On the other hand, ARCTIC agents achieve the value of the game against adversaries. To encourage ARCTIC to cooperate more against itself, we can introduce a small amount of risk in the initial round. From Figure 4, we see that ARCTIC achieves low levels of cooperation against themselves. When ARCTIC is equipped with a positive initial risk capital of 0 = 1, the ARCTIC agent is able to achieve more socially optimal outcomes against all players except for the adversary where it achieves marginally less than when 0 = 0. See results in Table 5.

CONCLUSION
In summary, we studied the trade-off between cooperation and safety, first showing how to unify these two objectives in the formalism of policy-conditioned beliefs and then characterizing a trade-off between them. We find that small risks to safety can lead to large returns in cooperation. We made this trade-off more intuitive through the idea of risk capital and seeing cooperation as the compounding returns on its investment. We use this intuition to build Accumulating Risk Capital Through Investing in Cooperation (ARCTIC) which enacts this trade-off, achieving safe cooperation in iterated prisoner's dilemma and stag hunt.
Cooperating while maintaining approximate safety allows us to design agents that individual developers would want to use out of their own self-interests. This is a promising development but leaves open questions that will be important in more complex environments. For instance, when there are many styles of successful cooperative strategies, agent designers would need to coordinate on a particular style of cooperation or build their agents to be adaptive to other agents' techniques. Moreover, although our method protects the agent against adversaries, it does not protect the agent against exploitative agents, who want to maximize their reward, which happens to come at the cost of our reward. Although an ARCTIC agent will not achieve less than − in expectation, they could be exploited into accepting less than their fair share of the reward as long as they receive more than . This becomes more complex when combined with the coordination problems, as different coordination solutions could have different payouts which must be somehow distinguished from exploitative strategies. Extending the ideas of risk capital to these settings is left to future work.
There are also interesting challenges in scaling ARCTIC to larger environments. Our method is currently reliant on both knowing an expected minimax value and a clear notion of cooperation. In larger environments, these are both less accessible. To extend ARC-TIC to these settings, either environment features would have to be estimated or the reliance on these features would have to be removed. Ultimately, addressing these issues could lead to general algorithms for safe multi-agent cooperation.


s2fieldsofstudy: Economics Sociology

text:
An overview of gender responsive budgeting in India

Gender budgeting has been well-acclaimed as a key tool for empowering women as it incorporates gender perspectives, and considers issues related to gender at all levels and phases of development, planning, policy making, programmes, and delivery mechanisms. Additionally, it effectively addresses gender gaps in budget allocations, especially in representative areas such as education, health, and employment. Gender budgeting aims at enabling access to resources and ensures that government resources are allocated fairly amongst all. Gender budgeting is thus creating new horizons of women empowerment. The concept of gender budgeting is becoming crucial and essential in the current Indian context as women are secluded, underestimated, and marginalised on numerous grounds.

Introduction
As women constitute half of the population in India, ensuring gender equality and empowerment of women are catalysts for the rapid economic advancement. As India is basically patriarchal, overall human development will only be possible with the empowerment of women who currently lag far behind their male counterparts in terms of education, health, earnings and decision making both at household and administrative levels. As per the Global Gender Gap Index, India was ranked 114 th out of 142 countries in 2015, and India keeps on slipping on the Global Gender Gap Index from 108 th position in 2019 to 112 th position among 153 countries in 2020. Sidelining women and girls in India from sharing the fruits of growth and development will thwart the prospects of the country in the long run as sustainable development of the economy largely relies upon the socioeconomic advancement of women. Hence, a nation which cherishes the dream of developed status cannot afford the underutilisation of female human resources which comprise 48.43 percent of total human resources. Therefore, opening up opportunities for women can play a prominent role in the process of accomplishing the developmental objective.
An overview of women in India demonstrates that they encounter various forms of vulnerability throughout the life cycle. Most of them face discrimination even before birth and it continues until their last breath; violence, harassment or abuse; neglect due to dependence and lack of access to resources; social prejudice; and exploitation; whether economic, political, social or religious are few to mention (Gender budgeting handbook, 2015). The violence against women and girls persists even today, both in private and public spheres. The brutality against women keeps on hiking, though it takes multiple formsdomestic violence, rape, trafficking, and child abuse, sexual harassment in the workplace, harassment and lack of safety in public places and while traveling. To tackle the issue of violence against women and also to ensure gender equality there is an urgent call for specific policy intervention from the government.
Gender responsive budgeting is one such initiative by the Government of India to ensure female empowerment.
Gender-responsive budgeting refers to a method of looking at the budget formulation process, budgetary policies and budget outlay through the gender lens. It is an analytical device which scrutinizes the government budget to uncover its gender-differentiated impact and advocates for greater priorities for projects and schemes to address the gender-based discrimination and disadvantages faced by women. The fundamental objective of gender budgeting is to refine budgets and related strategies to enable gender equality (Singh Awdesh Kumar, 2010).
Consequently, the Government of India introduced a National Policy for Empowerment of Women in 2001 which pointed towards achieving advancement and empowerment of women. The significant objectives of the policy were to eliminate all forms of discrimination against women and to ensure their dynamic participation in public and private spaces. Likewise, gender budgeting is also widely acknowledged as a powerful tool for empowerment of women. The union budget for 2005-06 included, for the first time, a separate statement on the gender sensitivity of budgetary allocations in ten demands for Grants. Since 2005-06, the Expenditure Division of the Ministry of Finance has been issuing a note on Gender Budgeting as a part of the Budget Circular every year which is compiled and incorporated in the form of Statement 20 in the Expenditure Budget Document.
The Gender Budget Statement (GBS) comprises two parts; Part A and Part B. Part A reflects Women-Specific schemes, which have a 100 percent allocation for women, while Part B reflects Pro-Women schemes, which include those with at least 30 percent of the allocation for women (Government of India, 2016). The Women-Specific Schemes and Pro-women Specific Schemes are spread across a broader spectrum as they incorporate women's need for shelter, security, wellbeing, legal aid, justice, information, maternal health, food, nutrition, as well as their need for economic sustenance through skill development, education and access to credit and marketing. Taking this into account, the paper analysed the trend and magnitude of budgetary allocations for schemes/programmes that substantially benefit women in gender budgeting statements in India from 2005-06 to 2020-21.

Gender budgeting as percentage to GDP in India
The overall Gender Budget has remained below 1 percent of the gross domestic product and less than 5 percent of the total expenditure during 2008-09 to 2020-21 (Table 1 and Fig. 1). The legitimacy of such allocations is another dimension to the issue of inadequate funds. Between 2008-09 and 2019-20, the actual or revised expenditure on schemes exclusively for women was lower than the budgeted estimates.  Table 1)

Magnitude of gender budgeting in India
Over time, the size of gender budgeting has been growing; in 2005-06 the Gender Budgeting Statement covered 10 Demands for Grants covering nine Ministries and Departments with an allocation of 24032 crores (2.79 percent of the overall budget). This covered 49 demands for grants from 38 ministries in 2019-20, pushing up its magnitude to 142813.3 crores (4.72 percent of the total budget). Gender budget as a percent to total budget was highest during 2011-12, (6.22 percent); but only 4.71 percent of the total budget in 2020-21 (Table 2). This is lower than 4.99 percent in 2018-19 and 5.28 per cent in 2017-18. But the share was more than double the share in 2004-05 (2.3 percent).

Allocations for women-specific schemes
Various ministries and departments have introduced certain women-specific programmes like Nirbhaya scheme, programmes for women's safety and security, maternity benefit schemes, programmes for women empowerment and so on that were exclusively meant for making women self-sufficient and for the provision for women safety (Table 3). Fund allocation for 100 percent of women-specific programmes covered 32.9 percent of the gender budget in 2005-06. It declined to 28.43 percent in 2010-11 and to 19.91 percent share of the gender budget in 2020-21. The funds are declining year after year and are inadequate to meet gender-specific needs, and the average percentage share of allocation under part A is 27.91 percent.

Composition of Women-specific programme
As far as women-specific schemes are concerned, in terms of numbers, there are only three schemes in the Gender Budgeting Schemes with an allocation of more than Rs 1000 crore. Within women-specific schemes, the majority of the expenditure goes to a rural housing, which accounts for around 68.26 percent of women-specific fund allocation and 8.75 percent for maternity benefit schemes (Table 4). The Ministry of Women and Child Development is the nodal agency for the implementation of the gender budgeting process. Various women-specific programmes undertaken by the Ministry of Women and Child Development include the Integrated Child Development Programme, Support for Training and Employment Programme (STEP), Rashtriya Mahila Kosh, Swadhar, Rajiv Gandhi Matrithva Sahayog Yojana (SABLA), and the National Commission for the Empowerment of Women. A look at the trends of the scheme on the basis of fund allocation depicts that the allocation to ICDS programmes only shows an increasing trend. The funding allocation to ICDS programmes in 2005-06 was Rs 3315.25 crore, but it was subsequently raised to Rs. 27584 crore in 2020-21 (Table 4).

Pro-women specific programmes
Pro-women specific programmes include those with at least 30 percent of the allocation for women or benefits reserved for women. Table 5 demonstrates the details about allocation of funds for pro-women specific programmes from 2005-2006 to 2020-21.  The percentage share of allocations under Part-A had always been lower than the allocation under Part-B. Part-B constitutes the maximum portion of the funds under the gender budgeting statement, which is not exclusively for women.

Conclusion
In India, gender budgeting has drawn significant attention from policymakers. With the production of the gender budgeting statement as part of the Union Budget documents, an institutionalized exertion has been made respond to the fundamental question of how much of the government's total expenditure is flowing to women. In spite of the fact that, the India government's experience of gender budgeting is widely used as a reference point for other gender budgeting initiatives, there seems to be an explicit gap between what was envisioned, and what has been accomplished under the rubric of gender budgeting.

Source of Funding
None.