Academic literature on the topic '3801 Applied economics'

The biological treatments are convenient and economical methods for improving the efficiency of industrial waste water treatment. Activated sludge is the most important biological wastewater treatment processes. Batch cultures for the treatment of whey with activated sludge were optimized through response surface methodology (RSM). A second-order central composite design was applied to evaluate the effects of three independent variables (aeration time, pH and percentage of sludge volume) on COD removal percent. Optimum conditions were estimated through RSM at 23.4 h aeration time, pH=6.8 and 5.6 (%v/v) sludge volume/ wastewater volume and to achieve 38.1% COD removal. 1 (g/L) addition of PAC reduction COD until 48.7%.

Most community enterprises in Thailand are not successful because of limited marketing channels and technology. The purpose of this paper is to examine the effect of platform capability and web capability on marketing effectiveness with a consideration of the mediating effect of digital marketing capability to community enterprises in Nakhon Pathom Province, Thailand. The study uses a multi-stage sampling method surveying 400 community enterprise members in Nakhon Pathom Province. The model applies and combines the concepts of dynamic capability. Data analysis uses structural equation modelling and the LISREL program for path analysis and testing the congruence of the causal relationship model. The causal model of factors influencing marketing effectiveness significantly fits the empirical data. The fit statistics are c2= 349.046, df = 245, p-value = 0.158, c2/df = 1.425, RMSEA = 0.024, NFI = 0.996, NNFI = 0.998, CFI = 0.996, RMR = 0.024, SRMR = 0.024, GFI = 0.933 and AGFI = 0.910. The results show that digital marketing is a full mediating effect of platform and web capabilities on marketing effectiveness of community enterprises. Community enterprises need to implement more online marketing channels for better performances. Moreover, the study serves as a guideline for developing community enterprises and the capability level of grassroots, which are a significant base of local economic development of Thailand. Importantly, grassroots development has been a national agenda of the Thai government. Keywords: Community enterprise, mediating effect, dynamic capability, marketing effectiveness, digital marketing.

Purpose The purpose of this paper is to assess the relative effectiveness of bilateral and multilateral concessional debts on economic growth in 32 sub-Saharan African (SSA) countries over the period 1985–2016. Design/methodology/approach The recently developed dynamic panel autoregressive distributed lag models which comprise three different estimators, the mean group, pooled mean group (PMG) estimator and dynamic fixed effect, were applied to estimate the model. Following these estimators, the Hausman test was employed to determine the efficient and consistent estimator. Findings The results showed that bilateral concessional debts had a negative impact on growth. From the findings, a 1 percent increase in bilateral concessional debts induced economic growth to decline by 38.1 percent points in the short run, and by 7.1 percent points in the long run; convergence to long-run equilibrium adjusted at the speed of 90 percent on an annual basis. Multilateral concessional debts were found to have a positive impact on growth both in the short and long run. The coefficient of the error term was negatively signed and indicates that deviations from the long-run equilibrium path were being corrected at the speed of 89.4 percent annually. Originality/value To the authors’ best knowledge, empirical studies that specifically seek to examine how bilateral and multilateral concessional debts impacted on growth are yet to attract the attention of researchers. As a result, this study will complement related extant growth studies, especially in the case of SSA.

A field experiment was carried out to study the effect of phosphorus (P) on kharif rice(Oryza sativa L.) in rice-greengram cropping system in Mid-Central Table Land Zone of Odisha. The experiment was conducted in split-plot design with three replications. Four main plots viz., P1-100% soil test dose (STD) for P as basal to rice, P2- 100% STD for P to both the crops, P3-50 % STD for P to rice and 100% STD for P to greengram, P4 - No P to rice and 100% STD for P to both the crops was applied to greengram, were allotted to the main plots and three rice varieties viz., V1-Mandakini, V2-Lalat and V3- Pratikhya were considered in the sub plots. Application of 100% STD for P to both the crops (P2) resulted in the highest grain and straw yield of 3980 and 4715 kg/ha, respectively. Rice variety ‘Pratikshya’ produced the highest grain and straw yields of 3808 and 4399 kg/ha, respectively. Similarly, the same P management practice and rice cv. ‘Pratikshya’ recorded the highest chlorophyll content (3.54 mg/g of fresh leaf weight), nutrient uptake (N 68.9 kg/ha , P 27.2 kg/ha , K 88.2 kg/ha ) gross and net returns and B: C ratio (1.94). However, the post harvest soil nutrients status were maximum with P2 where rice cv. ‘Mandakini’ was taken.

The notion of self-sufficiency (Greek autarkeia) is gaining prominence in the context of probably the most pressing dilemma of our times, namely that of the conflicting demands of economic growth and ecological sustainability. Within this controversy, self-sufficiency is promoted as a viable counter-ideal to rampant consumerism. This article presents a survey of the use of the notion in ancient Greek literature of the classical era, in order to show that, by itself, autarkeia does not present a simple solution, due to the variety in its ancient usage. While the Greeks of archaic and classical times widely agreed on the desirability of the condition, some interpreted it as being able to fulfil any need that might arise and others as restricting need to the bare minimum. The notion was furthermore applied to both individual and state. There was no consensus that the individual could in fact reach a state of complete self-sufficiency: the radical but experimental autarkeia of the Cynic sage was admired but nonetheless generally rejected as incompatible to civil society. Consequently, authors of the fourth century transposed autarkeia to the social units of household and city-state, although even here its attainability remained dubious. The notion lived on in the restricted form of the self-sufficiency of virtue in the Stoic pursuit of happiness.

One of the most economical ways of whey processing is the production of beverages, that represents a single process that exploits all the potential of whey as a raw material. Functional and sensory characteristics of whey based beverages are a criterion that is crucial to the marketing of products and win over consumers. The aim of this study was to determine nutritional and functional characteristics of fermented whey beverage with milk and commercial ABY-6 culture. The results showed that the applied starter culture can be used for the production of fermented whey based beverage with satisfactory nutritional properties. Addition of milk was important not only in the nutritional quality of the resulting product, but also improved the taste, the homogeneity and stability. Analysis of the chemical composition of fermented whey based beverage and nutritional information about it indicates that the product is a good source of protein and calcium. Fermented beverage contained 8.07 log (CFU/mL), showed antioxidant activity of at least 38.1% and the titratable acidity of 28.2?SH corresponding to the acidity of the product in this category.

Financial reporting timeliness is one of the characteristics that enhance and improve the quality of useful financial information, which can affect stock prices. Given the importance of financial reporting timeliness, this research extends prior studies regarding determinants of, and stock market reactions to, financial reporting lag. However, the impact of tax-related variables (related party transactions, capital structure and tax audits) on financial reporting lag was analysed in this as well as other determinants (audit report lag, firm size, profitability, and audit opinion). The related party transactions and a high level of debt on capital structure are notoriously popular for achieving tax benefits and possibly considered as bad news. Investors could consider that gaining tax benefits or minimising tax payment as a negative behaviour in business. This study uses a stratified random sampling method to obtain the data from various industry sectors on the Indonesia Stock Exchange (IDX) from 2014 to 2017. The sample consists of 468 firm-year observations. Two-stages least squares regression method, the OLS model, and dynamic GMM model were used to analyse the relationship between stock market reactions and financial reporting lag. In addition, the least square model and Wald test were also used to analyse the asymmetric stock market reactions between timely and late financial reporting lags. Using LASSO Regression, the findings show that leverage, related party transactions, and tax audits are found to have no relationship with financial reporting lag. These findings indicate that the tax-related variables do not affect financial reporting timeliness. This means that the IDX firms do not consider related party transactions, high level of loan on capital structure, and tax audit results as bad news. Also, profitability and audit opinion have no relationship with financial reporting lag. Meanwhile, audit report lag and firm size are the variables, which are found to show a relationship with financial reporting lag. Moreover, the Wald tests on least square model reveal some evidence about asymmetric stock market reactions between timely and late financial reporting lag. Also, the data analysis using two-stage least-square model, the OLS model, and the dynamic GMM shows significantly negative relationship between predicted financial reporting lag and stock market reactions. However, the dynamic GMM model presents better results than those from the two-stage least square model and the OLS model due to the endogeneity problem on panel data used in this study. The results are consistent with the semi-strong form of the efficient market hypothesis. It indicates that the stock markets react to the publicly available information including prior stock prices and annual financial reporting during the event windows. The academic contributions of this study are as follows: 1. Investigating the audit report lag and tax-related variables into financial reporting lag and stock market research for emerging economies. 2. Selecting samples from various industry sectors for stock market reactions to financial reporting lag because prior studies used the sample from listed manufacturing firms in Indonesia. 3. Applying the two-stage least square method and the dynamic GMM model to analyse the stock market’s reactions to financial reporting lag because this method considers and tackles the endogeneity problem experienced in the model particularly on the panel data by the dynamic GMM model. 4. Using the Wald test to analyse the asymmetric stock market reactions between timely and late financial reporting lag. Finally, the practical contributions of this study are as follows: 1. The Financial Service Authority of Indonesia (OJK) could enhance its supervision toward the non-compliant firms in submitting their annual financial reports. 2. Investors may discover that publicly listed corporations do not take related party transactions, a high degree of debt on a capital structure, and tax audit results into consideration when releasing their annual financial reports. As a result, to make an investment choice, investors do not need to seek information about listed corporations declaring those accounts. 3. The investors also may find the appropriate timeliness to invest or divest their money from the timely and late financial reporting firms. 4. The companies’ managers could assess the impact of timely and late financial reporting of the listed firms. 5. The findings of this study have implications for investors in countries, which have similar financial reporting and tax regulations to Indonesia.

The Kingdom of Saudi Arabia has experienced major crises regarding the lower representation of the local workforce in its labour market and the Government has implemented a policy of Saudisation to encourage private sector employers to employ locals. However, a higher proportion of expatriate workers in the Saudi labour market remains and local Saudis are making up a large part of public sector organisations. Saudi representation is lacking in the private sector. For example, the wholesale-retail and construction sectors have 88.3% and 80% of foreign employees respectively. This research explored the factors which are impeding employment of a local workforce in the wholesale-retail and construction industries and contrasting these factors across the two sectors and to identify the causes behind the ineffectiveness of the Saudisation policy. To identify the factors underlying the underrepresentation, the study relied on a mixed research methodology. A survey of employees was conducted as part of the quantitative research and interviews from owners and managers made up the core of the qualitative research. It was found from the quantitative research that the key contributing factors are: organisational commitment, working conditions, wages/benefits, social status and power distance. There was a statistically significant difference for these factors across wholesale-retail and construction sectors, except for social status. The perceptions of these factors were higher for the wholesaleretail sector, as service-based jobs are less preferred by Saudis and they view them as less privileged compared to other sectors. Statistically significant differences were also found for the factors affecting the employment of Saudis in the wholesale-retail and construction industries across the demographic variables of gender, work experience, training and scientific qualification. The findings of the qualitative study have revealed the causes behind the ineffectiveness of the Saudisation programme. The key reasons included the cultural factors (social status and power distance), the ineffectiveness of the education system (creates a gap in demand and supply of skills and knowledge for labour market), ineffective training and high inclination of local employees towards the public sector. The study provides practical guidance to policymakers, employers, Saudi employees and implementers of the Saudisation programme. The factors leading to the slow response of the private sector in the implementation of Saudisation are identified. Suggestions are made for addressing the factors concerning the employees by supporting the private sector and by reducing the pay gap between private and public sectors to encourage local employees to pursue careers in these sectors. There is a need for continuous collaboration between all sectors in the economy for prosperity to be achieved. Employers should address attitudinal and motivational factors and the aspect of power distance. This study is the first to introduce the notion of power distance and social status as the cultural factors influencing the hiring of Saudi nationals. It provides a good background for future research.

Housing market price prediction is a major and important challenge in economics. Since the 2008 global financial crisis, researchers, economists, and politicians around the world have increasingly drawn attention to the need of better understanding housing market behaviour, since the failure to predict housing market crisis ahead of time had led to catastrophic global damage. On the other hand, around the same time, we have seen the revolution of information technology and artificial intelligence in the last two decades. The advent of powerful cloud and high performance computing systems, big data, and advanced machine learning algorithms have demonstrated new applications and advantages in cutting-edge research and technology areas such as pattern recognition, bioinformatics, natural language processing, and product recommendation systems. Can we make the leap of improving our understanding of housing market behaviour by leveraging these recent advances in artificial intelligence and newly available big data? This is the main theme of the thesis. There is strong motivation to explore the application of data science methods, including new large datasets and advanced machine learning algorithm, to accelerate our understanding of housing market problems for the benefit of the common good. In order to understand housing market behaviour, we divide the problem into two major steps: first, to improve understanding of housing appraisal (at microlevel), which is to predict housing price at the point level given a fixed timeframe; second, to improve understanding of the trend prediction (at macro level), which is to predict the housing price trend for a specific place during a time interval. For these two major steps, we improve upon traditional economic modelling by: • Adding new, non-traditional variables/features to our models, such as location-based Point of Interests, regional economic clusters, qualitative index, searching index, and newspaper articles • Applying machine learning algorithms for data analysis, such as non-linear algorithms, K-Nearest-Neighbour, Support Vector Machine, Gradient Boost, and sentiment analysis Specifically, in Chapter 3, we focus on the development of Location-Based Social Network (LBSN) for our micro-level housing appraisal modelling. A good location goes beyond the direct benefits from its neighbourhood. By leveraging housing data, neighbourhood data, regional economic cluster data and demographic data, we build a housing appraisal model, named HNED. Unlike most previous statistical and machine learning based housing appraisal research, which limit their investigations to neighbourhoods within 1km radius of the house, we expand the investigation beyond the local neighbourhood and to the whole metropolitan area, by introducing the connection to significant influential economic nodes, which we term Regional Economic Clusters. Specifically, we introduce regional economic clusters within the metropolitan range into the housing appraisal model, such as the connection to CBD, workplace, or the convenience and quality of big shopping malls and university clusters. When used with the gradient boosting algorithm 2 XGBoost to perform housing price appraisal, HNED reached 0.88 in R . In addition, we found that the feature vector from Regional Economic Clusters alone reached 0.63 in R2, significantly higher than all traditional features. Chapter 3 focuses on the exploration and validation of HNED modelling. In Chapter 4 and Chapter 5, we focus on macro-level housing price trend prediction. We fill the gap between the traditional macro-level housing market modelling and new developments of the concept of irrationality in microeconomic theories, by collecting and analysing economic behavioural data, such as real estate opinions in local newspaper articles, and people’s web searching behaviour as captured by Google Trend Index. In Chapter 4, we discuss the usage of micro-level behavioural data for understanding macro-level housing market behaviour. We use sentiment analysis to examine local newspaper articles discussing real estate at a suburb level in inner-west Sydney, Australia. We then calculate the media sentiment index by using two different methods, and compare them with each other and the housing price index. The use of media sentiment index can serve as a finer-grained guiding tool to facilitate decision-making for home buyers, investors, researchers and policy makers. In Chapter 5, we discuss how new developments of behavioural economic theory indicate that the information from decision-making at the micro-level will bring a new solution to the age-old problem of economic forecasting. It provides the theoretical link between irrationality and big data methods. Specifically, Google Trend Index is included as a new variable in a time series auto-regression model to forecast housing market cycles. To summarise the contributions of the thesis, we conclude that this is a successful early attempt to study housing price problems using data science methods, by leveraging newly available data sets and applying novel machine learning methods. Specifically, location-based social data improves the housing appraisal modelling. Human behaviour for housing market is analysed by introducing local newspaper articles and Google Trend Index into the modelling and analysis.

Electronic waste or e-waste is a global challenge of increasing significance because of the volume being generated and complexity of this waste. Pakistan, like some other developing economies, generates a significant volume of e-waste and also imports it for processing despite lacking the necessary infrastructure. While e-waste is a business opportunity for the valuable materials that are recovered, at an ecosystem level there is great cause for concern over the use of informal practices. This study is a critical analysis of e-waste management and recycling in Pakistan, using a Life Cycle Assessment (LCA) to examine structures, processes and costs of upstream e-waste generation1 and downstream disposal and recycling. Domestic e-waste generated annually in Pakistan is about 1,790 kilo-tons (2018-2019) and is expected to grow at 10.2% annually. Annual imported e-waste is reported to be about 95,145 tons (2011-2014 figures). Of the collective total, about 8.6% (154.8 kilo-tons) is processed using informal practices, while the remaining goes directly into landfill (13.8% or 245.6 kilo-tons), sold/given away for reuse (65.7% or 1,150 kilo-tons), or stored, which is a deferred disposal strategy. Analysis shows that informal recycling is profitable for both dismantling businesses (benefits are 1.19-1.27 times the costs) and extracting/refining businesses (benefits are 1.95-2.22 times the cost). For recycling workers, net economic costs per worker of about Rs.34,069 - 85,478 (USD 203–510) per month, exceed any economic benefit 2.6-4.7-fold. A methodological contribution is the application of systems thinking and causal loop modelling to strategy formulation at an industry level. A causal map shows overlapping interests and dynamic relationships in e-waste recycling in Pakistan. Four causal loops are identified. They collectively sustain current local practices in e-waste recycling. A systems-level view and pictorial representation of the e-waste situation helps to reveal contentious aspects of informal recycling, and also leverage points to improve e-waste recycling and re-engineer product development. This open-box approach, where the inner workings are exposed and illuminated, can also help policy- makers trace the chain of causality from product design to waste disposal at a fundamental level and enables consideration of primary prevention strategies at the various levels, including producers, consumers, and recyclers. Guided by the principle that what gets measured can be managed, a practical contribution of this thesis is to present a synthesis of upstream consumer and downstream business considerations. In sum, the informal recycling industry is financially lucrative, but sustained by a lack of regulatory frameworks and limited accountability for known and hidden first-, second- and third-order effects on people and the environment. These externalised costs are not measured. The industry in Pakistan can be conceptualised as being based on local practices contingent on waste value, business opportunity and perceived choice. Leverage points for fundamental, longer-term change in processes associated with waste generation, and for management and disposal of e-waste have been identified. The study develops a consolidated impact factors framework (CIFF) based on financial and non-financial (social) variables to support an analysis of factors that influence upstream generation and downstream processing of e-waste. A multi-level impact assessment framework (MIAF) and related findings can be generalised to other regions in Pakistan and to other countries that similarly generate and import e-waste, and use informal practices to process this material. Crucially, for the necessary focus on the many aspects of e-waste, the starting points are to document, regulate and go beyond return on investment when assessing performance to select multiple criteria and measure what matters.

Abstract Under the condition of high ambient temperature and high confining pressure，the physical & mechanical properties and in-situ stress state of deep shale will change noticeably. Normally, the deep-shale formation has high horizontal stress difference (about 11∼21 MPa, 1595∼3045 psi), high fracture-closure pressure gradient (about 0.023∼0.025 MPa/m, 1.017∼1.105 psi/ft), high breakdown pressure gradient (larger than 0.03 MPa/m, 1.327 psi/ft), low mechanical brittleness (about 42%∼55%), low difference between the vertical and the horizontal stresses (about 3∼5MPa, 435∼725 psi). The complex geological characteristics of deep shale increase the difficulity of fracturing: 1) effect of brittle/ductile transition under high confining pressure; 2) non-uniform propagation of multi-cluster fractures is more prominent; 3) the migration of proppant is difficult in narrow fracture network; 4) high friction and high pumping pressure; 5) more stringent requirements for fracturing tools; 6) high requirements for fracturing scale, efficiency and economy. To address above challenges, this paper presents a comprehensive overview of latest researching and applicable techniques about deep-shale fracturing (3500<TVD<3800 m, 11482∼12467 ft), including: 1) new evaluation methods on fractured shale quality and fracability, considering vertical stress difference coefficient and effective confining stress; 2) non-uniform propagation of fractures in multi-clusters perforation; 3) reveal the transport mechanism of proppant in narrow fracture network; 4) optimization of high performance fracturing fluid systems to enlarge the ESRV in deep shale; 5) development of a new staged fracturing tool for deep-shale fracturing, including dissoluble bridge plug and toe delayed sleeve; 6) an integrated geoscience and engineering simulation to optimize the treatment parameters and to achieve the best fracturing efficiency in the deep shale strata. The hydraulic fracturing technique for deep shale gas with the depth of 3500∼4500 m (11482∼14763 ft) has formed preliminarily. The hydraulic fracturing technology for deep shale gas (TVD≥3500∼3800 m, 11482∼12467 ft) have made a breakthrough in Sichuan basin, China, and significant progress has also made in 3800-4500m TVD (12467∼14763 ft). The research results and techniques introduced in the paper have been successfully applied to more than 100 wells in the Sichuan basin. The test production of part fractured well can reach (10∼31)×104 m3 per day (0.35∼1.09×107 SCF/day), which basically realizes the economical and effective development for deep shale gas.

To extend the life of a CANDU-6 reactor beyond its original design life requires a major investment for the replacement of reactor components (380 pressure and calandria tubes). After a preliminary technical and economical feasibility study [ref 1], Hydro-Quebec, owner of the Gentilly-2 NPP, has decided to perform a more detailed assessment to: • Get assurance that it is technically and economically viable to extend Gentilly-2 for another 20 years beyond the original design life; • Identify the detailed work to be done; • Define the overall cost and the general schedule; • Ensure an adequate licensing strategy to restart after refurbishment; • Complete all the Environmental Impact Studies required to obtain the government authorizations. Two processes have been used to assess the “health” of the station Systems, Structures and Components (SSCs): • The Plant Life Management Studies (PLIM) for approximately 10 critical SSC or families of SSC (PLIM Studies); • The Condition Assessment Studies for other SSC with a lower impact on the Plant production or safety. These two processes are briefly presented in the paper, as they were realized and applied at Gentilly-2 NPP.

Thermoeconomic optimization is a relatively modern technique to analyze and design more efficient energy systems, leading to a better compromise between energetic efficiency and cost. Thermoeconomic optimization can be parametric (plant configuration is fixed), applicable both at the design phase or the operation phase of an energy system, or structural (plant configuration may vary). In practice, mathematical thermoeconomic optimization may be accomplished in two ways: (i) the conventional way, which manipulates all pertinent equations simultaneously, or (ii) integrated with a professional process simulator, such that the equations are manipulated separately. In the latter case, the simulator deals with the thermodynamic property and balance equations, while an external optimization routine, linked to the simulator, deals with the economic equations and objective function. In this work a previous implementation of an integrated approach for parametric mathematical thermoeconomic optimization of complex thermal systems is applied to an actual combined-cycle cogeneration plant located in the outskirts of the city of Rio de Janeiro, in Brazil. The simulator is the Thermoflex software, which interfaces with the MS-Excel program. Thus, the Powell’s method for optimization integrated with Thermoflex is written in the VBA language. The plant contains more than 60 thermal components, including two gas turbines, one steam turbine, and two heat recovery steam generators. Simulation of one operational condition of the plant requires several hundred variables. The plant produces nominally 380 MW of power, and exports a mass flow rate between 200 and 400 ton/h of superheated process steam, at 45 bar and 404°C, to a neighboring refinery. The cogeneration plant operates subjected to an economic scenario, which changes with time, because of varying fuel, electricity, and steam prices. Therefore, to manage the plant, it is important to know the minimum operational cost, when a fixed contracted hourly-rate of process steam has to be exported, while a variable amount of electrical power is produced. An optimization problem can thus be formulated, for which the objective is to minimize the cost of consumed resources per unit electrical power generated. Results of optimization exercises to determine the optimal operational conditions of the plant for various exported mass flow rates of process steam are presented and discussed.

Abstract This paper provide several improved miscible assistants, trying to mitigate the problem that CO2 miscible flooding is difficult to achieve in reservoirs because of the high miscible pressures, which leads to a lower recovery up to expectations. These miscible assistants could be easily mixed with crude oil by adding into CO2 and reduce the interfacial tension to drive down the minimum miscible pressure (MMP) in order to enhance sweep efficiency. Some efforts have been made to improve this situation. The effective method is to draw the experiences from structure characteristics of surfactants applied in micro emulsion of CO2-water systems. Amphiphilic organic assistants were designed to synthetize with fluoro-alkane chains and non-fluoro-OAc chains as the CO2-philic ends, as well as alkane structure as the lipophilic ends. The minimization of MMP of CO2 miscible processing assistants has been analyzed and optimized by surface tension testing in CO2-kerosene system and CO2-crude oil system. The crude oil was obtained from field test pilot in L Reservoir. The results of interfacial tension tests show that per-acetylated glucose dodecyl ester molecules have the ability to lower the interfacial tension most in these five kinds of new miscible processing assistants in two categories. Citric acid triisopropyl ester molecules take the second place, and others almost make no difference. The probable reason is that kerosene is mainly composed of C12 and lack of heavy components, which cause a weak interaction between independent hydroxyl of citric acid and tartaric acid assistant and hydrogen bond of kerosene. The results of interfacial tension tests show that all these miscible assistants possess good effects on minimizing the interfacial tension of the CO2-crudes system, and could also definitely reduce the MMP. Among these, per-acetylated glucose dodecyl ester molecules and citric acid isopropyl ester molecules perform most excellent, and could decrease the MMP of CO2 flooding by 27.5%. The assistants have been implemented in the CO2 flooding plan of L Reservoir with 4 gas injectors and 15 producers. After 15 years’ development simulation, cumulated oil production will reach 3.4 MMbbl with recovery increasing from 41.6% (only CO2 flooding) to 46.6%. Injection test shows that 1400 tons CO2 has been injected with 3800 bbl oil produced. The improved miscible assistants provided perform as well as other existing assistants in reducing interfacial tension and enhancing sweep efficiency in CO2 flooding. Compared with assistants of light hydrocarbon, these assistants require a little quantity to improve the miscible flooding, which could break the economic limits. Compared with the traditional fluoride assistants, these assistants are quite different in molecular structure and could cause little pollution and have been applied in field test.