Growth Rate Of Gross Domestic Product Research Articles

Analysis of the Effects of Money Market Fund Mobilization on the Dynamics of Nigerian Economic Growth

Economic growth is a function of some productive efforts among which savings mobilizations is considered very vital. This study examines savings mobilization strategy and its impact on Nigeria’s economic growth during the year 2001-2022.This is vested with the objectives to investigate the effect of banking density, savings rates and money supply on  economic growth. The study relies on time series data sourced from the publication of the Central Bank of Nigeria. Gross Domestic Product Growth Rate (GDPGR) was adopted as dependent variable, while Banking Density (BD), Savings Rates (SR) and Money Supply (MS) were the independent variables. Augmented Dickey Fuller (ADF) unit root test was employed, to test the stationarity. The Auto Regressive Distributive Lag (ADRL) was used to ascertain the relationship between the variables alongside Vector Error Correction Model (VCM). Post estimation diagnostic tools used include Breuch-Godfrey serial correlation LM test and the CUSUM test for stability. From the ARDL, β-coefficient and the associated probabilities were adopted to determine the extent and direction of relationship on economic growth. Data were tested at 5% level of significance; it was discovered that banking density and savings rates affected economic growth positively but with insignificant effects, while money supply affected economic growth negatively with insignificant effect. The study therefore recommends amongst others, that stakeholders, be directed towards entrenching higher banking density as opposed to banking desert, and that money supply should be made dynamic in accordance with economic realities.

Foreign Aids, Human Capital and Economic Growth

This research work examines the impacts of foreign aids and human capital on economic growth of African oil and non-oil producing countries. Adopting the time series data from 2000 to 2021, the study employs Westerlund Error Correction Based panel cointegration test to investigate the impacts of Physical Capital (FA, GCF, FDI), Human Capital (EDU, HLT) and Policy Variables (REV, EXR) on Gross Domestic Product growth rate (GDPgr). Findings from results of the study affirms that there is a long-term co-movement between the GDPgr and the explanatory variables (FA, GCF, FDI, EDU, HLT, REV, EXR) of both African oil and non-oil producing countries. The study also reveals that foreign aids are better effectively put into use in the African non-oil producing countries than in the oil producing countries. Findings in the study equally confirm that financial resources in the African non-oil producing countries are better efficiently spent on human capital and domestic investment than in the African oil producing countries. The study further shows that revenue generation and foreign exchange earnings stimulate growth better in the African oil producing countries than the non-oil countries counterparts in Africa.  

Savings Mobilization Efforts of the Nigerian Money Market and Economic Growth

Economic growth is a function of some productive efforts among which savings mobilizations is considered very vital. This study examined saving mobilization efforts of the Nigerian money market and the effects on economic growth during the year 2001 – 2022.Vested with the objectives to investigate the effect of banking density, savings rates and money supply on   economic growth. The study relied on time series data sourced from the publication of the Central Bank of Nigeria. Gross Domestic Product Growth Rate (GDPGR) was adopted as dependent variable, while Banking Density (BD), Savings Rates (SR) and Money Supply (MS) were the independent variables. Augmented Dickey Fuller (ADF) unit root test was employed, to test the stationarity. The Auto Regressive Distributive Lag (ADRL) was used to ascertain the relationship between the variables alongside Vector Error Correction Model (VCM). Post estimation diagnostic tools used include; Breuch-Godfrey serial correlation LM test and the CUSUM test for stability. From the ARDL, β-coefficient and the associated probabilities were adopted to determine the extent and direction of relationship on economic growth. Data were tested at 5% level of significance, it was discovered that banking density and savings rates affected economic growth positively but with insignificant effects, while money supply affected economic growth negatively with insignificant effect. The study therefore recommends amongst other, that stakeholders, be directed towards entrenching higher banking density as opposed to banking desert, and that money supply should be made dynamic in accordance with economic realities.

Predicting Gross Domestic Product (GDP) using a PC-LSTM-RNN model in urban profiling areas

Gross Domestic Product (GDP) is significant for measuring the strength of national and global economies in urban profiling areas. GDP is significant because it provides information on the size and performance of an economy. The real GDP growth rate is frequently used to indicate the economy’s health. This paper proposes a new model called Pearson Correlation-Long Short-Term Memory-Recurrent Neural Network (PC-LSTM-RNN) for predicting GDP in urban profiling areas. Pearson correlation is used to select the important features strongly correlated with the target feature. This study employs two separate datasets, denoted as Dataset A and Dataset B. Dataset A comprises 227 instances and 20 features, with 70% utilized for training and 30% for testing purposes. On the other hand, Dataset B consists of 61 instances and 4 features, encompassing historical GDP growth data for India from 1961 to 2021. To enhance GDP prediction performance, we implement a parameter transfer approach, fine-tuning the parameters learned from Dataset A on Dataset B. Moreover, in this study, a preprocessing stage that includes median imputation and data normalization is performed. Mean Square Error, Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, Median Absolute Error, and determination coefficient (R2) evaluation metrics are utilized in this study to demonstrate the performance of the proposed model. The experimental results demonstrated that the proposed model gave better results than other regression models used in this study. Also, the results show that the proposed model achieved the highest results for R2, with 99.99%. This paper addresses a critical research gap in the domain of GDP prediction through artificial intelligence (AI) algorithms. While acknowledging the widespread application of such algorithms in forecasting GDP, the proposed model introduces distinctive advantages over existing approaches. Using PC-LSTM-RNN which achieves high R2 with minimum error rates.

Linkage vector autoregressive model

AbstractTo accommodate linkage effects of individuals, we develop a new linkage vector autoregressive (LAR) model for dynamic panel data. A main feature of the LAR model is incorporating dynamic network information in autoregressive time series modeling. The dynamic network can be given, or we can formulate the network links as a function of historical data, where unknown parameters of the function can be estimated from the data. We propose a simulation technique to check the stationarity condition of the LAR model and suggest a Bayesian Markov chain Monte Carlo method for estimation. Empirical results for the quarterly growth rates of gross domestic product (GDP) in 45 countries indicate that (i) the autoregressive (AR) coefficient for the aggregated growth rate is hard to distinguish from zero; (ii) a panel AR model with individual effects has a positive autocorrelation; and (iii) the alternative LAR models are preferred to the panel AR model. Depending on the specification of the linkage variables, the sign and size of the linkage effect can differ. The logistic linkage function has a flexible structure to accommodate the size of the linkage of individual GDP growth rates, and it strengthens the dynamics.

ВАЛОВИЙ ВНУТРІШНІЙ ПРОДУКТ ЯК ІНДИКАТОР РІВНЯ ЕКОНОМІЧНОГО РОЗВИТКУ КРАЇНИ (НА ПРИКЛАДІ ЧЕСЬКОЇ РЕСПУБЛІКИ)

The article addresses the current issue of researching and analyzing Gross Domestic Product (GDP) as an indicator of national economic competitiveness on a global scale and its impact on the macroeconomic stability of the Czech Republic. The authors emphasize the importance of GDP analysis for effective economic management, planning, forecasting, and ensuring macroeconomic stability. The study is based on a review of previous research and publications by both Ukrainian and foreign scholars. As a result, the concept of Gross Domestic Product (GDP) is thoroughly analyzed, with three main aspects of the concept elucidated: "gross," "domestic," and "product." Furthermore, the article provides crucial explanations to enhance comprehension of the GDP concept. Moreover, the article extensively analyzes the dynamics of nominal and real Gross Domestic Product (GDP) of the Czech Republic from 2016 to 2022. Special attention is given to the impact of the COVID-19 pandemic on the Czech economy and its adverse effects in 2020. The causes of GDP decline are highlighted, along with data on economic recovery at the end of 2020 and an assessment of GDP dynamics in 2021 and 2022. The significance of evaluating GDP growth rates and their potential implications for the Czech economy are underscored, as well as the importance of GDP per capita as an indicator of Czech economic competitiveness and the country's position in the GDP per capita ranking. Additionally, the article examines the impact of declining household consumption on Czech GDP due to inflation and low investments. The methodology of correlation-regression analysis is employed to identify trends in GDP dynamics, along with projected GDP values for 2024 and 2025. It is noted that the main drivers of Czech economic growth should be the export activities of companies and the recovery of household demand. However, further research is needed to explore the analysis of GDP dynamics as an indicator of macroeconomic stability, its impact on forming global competitiveness rankings, and positioning the Czech Republic in the global economic community.

The Impact of Macroeconomic Factors on Bank Credit Risk

Credit risk has become one of the main risks faced by banks under the background of increasing loan demand. Meanwhile, bank credit risk may be affected by a number of factors, including the expansion rate of Gross Domestic Product (GDP), changes in interest rate, unemployment rate and even more. These macroeconomic factors are crucial in determining how credit risk for banks is shaped. This paper concludes the different impacts of different macroeconomic factors discussed in several papers on the risks of bank credit, and illustrates how changes in GDP growth rate and rate of interest affect credit risks. Through comprehensive literature review and empirical research, this paper finds that macro factors, including the expansion rate of GDP and the rate of interest, have considerable impacts on banks' credit risk. Bank credit risk will decrease as GDP growth rate increases; however the risk of bank credit will increase as interest rates rise. At the end, the paper provides several suggestions for the development of risk management strategies, including the monitoring of macroeconomic factors and the adjustment of credit policies under different economic situations. The research results are intended to help researchers in related fields understand current research findings and patterns of development in the research field, to give a theoretical foundation and backing for their investigations, and to offer important guiding significance for banks and relevant regulators to formulate risk management strategies.

The Impact of COVID-19 on Chinas Quarterly Economy Growth Rate: Evidence from ARIMA Model

The COVID-19 pandemic unleashed unprecedented economic turmoil worldwide, with China, as its epicenter, experiencing profound repercussions. This study investigates the pandemic's impact on China's Gross Domestic Product (GDP) growth rate, considering macroeconomic, microeconomic, and societal dimensions. COVID-19 disrupted international trade, manufacturing, and supply chains, causing significant economic contractions. China's resilient response strategies, encompassing fiscal policies, digital transformation, and e-commerce promotion, played a pivotal role in stabilizing the economy. Utilizing ARIMA modeling and credible data from the National Bureau of Statistics of China, this research quantitatively assesses the pandemic's effects. It highlights the necessity for adaptable economic models that incorporate dynamic societal changes during crises. Policymakers can draw valuable insights from this study for crafting effective crisis response strategies, while investors can identify sector-specific opportunities in technology and related industries, influenced by shifts in consumer behavior. In summary, this study offers a comprehensive analysis of COVID-19's impact on the growth rate of China's GDP, providing a methodological framework, policy implications, and investment insights. As global economies recover from the pandemic's aftermath, these findings will continue to guide decision-makers in building resilient and adaptable economies for the future.

Comparative analysis of Fuzzy Regression and Fuzzy ARIMA models for forecasting real gross domestic product of Nigeria

Over the years, a number of studies have been conducted in terms of forecasting the real gross domestic product (GDP) of Nigeria. The GDP growth rate measures the annual growth rate in percentage of the monetary value of all finished goods and services made within a country. It is an important indicator of economic growth. This paper presents Fuzzy ARIMA and Regression to determine the interval of possibility for predicting the real GDP. The Fuzzy ARIMA (FARIMA) and Fuzzy Regression (FR) methods requires small size data as compared to the classical time series. Comparison between FARIMA and Fuzzy Regression with a threshold level of zero (ℎ=0) is performed by calibrating the models on existing data. The minimum values of the total spreads for FARIMA and FR are 0.791 and 4.077 respectively. In addition, the MAPE values for FARIMA is smaller than that of FR. Furthermore, the results indicate that the FARIMA gives a narrower interval of possibility for prediction than the FR.

The clean energy development path and sustainable development of the ecological environment driven by big data for mining projects

Clean energy is urgently needed to realize mining projects’ sustainable development (SD). This study aims to discuss the clean energy development path and the related issues of SD in the ecological environment driven by big data for mining projects. This study adopts a comprehensive research approach, including a literature review, case analysis, and model construction. Firstly, an in-depth literature review of the development status of clean energy is carried out, and the existing research results and technology applications are explored. Secondly, some typical mining projects are selected as cases to discuss the practice and effect of their clean energy application. Finally, the corresponding clean energy development path and the SD analysis model of the ecological environment are constructed based on big data technology to evaluate the feasibility and potential benefits of promoting and applying clean energy in mining projects. (1) It is observed that under different Gross Domestic Product (GDP) growth rates, the new and cumulative installed capacities of wind energy show an increasing trend. In 2022, under the low GDP growth rate, the cumulative installed capacity of global wind energy was 370.60 Gigawatt (GW), and the new installed capacity was 45 GW. With the high GDP growth rate, the cumulative and new installed capacities were 367.83 GW and 46 GW. As the economy grows, new wind energy capacity is expected to increase significantly by 2030. In 2046, 2047, and 2050, carbon dioxide (CO2) emissions reductions are projected to be 8183.35, 8539.22, and 9842.73 Million tons (Mt) (low scenario), 8750.68, 9087.16, and 10,468.75 Mt (medium scenario), and 9083.03, 9458.86, and 10,879.58 Mt (high scenario). By 2060, it is expected that CO2 emissions reduction will continue to increase. (2) The proposed clean energy development path model has achieved a good effect. Through this study, it is hoped to provide empirical support and decision-making reference for the development of mining projects in clean energy, and promote the SD of the mining industry, thus achieving a win-win situation of economic and ecological benefits. This is of great significance for protecting the ecological environment and realizing the sustainable utilization of resources.

Effect of FDI Net Inflow on the GDP growth rate: 1990-2021

Purpose: This study examines the impact of foreign direct investment (FDI) on the gross domestic product (GDP) growth rate in Nigeria from 1990 to 2021. Research methodology: This study employed time-series data from the World Bank’s World Development Indicator Database to ensure data homogeneity. The data were assessed for stationarity using the ADF unit root test. All the data were stationary after the first difference. The hypothesis was tested using ordinary least squares (OLS) with BLUE properties. Results: The study finds a significant positive effect of foreign direct investment net inflow on GDPGR (p<.05). The control variables DEGO (p=0.3299) and INFL (p=0.3321) showed positive coefficients but were non-significant. Limitations: The study used only data from the Nigerian context, from 1990 to 2021, which affects the generalizability of the study findings to other countries in SSA, and a limited number of control variables, such as DEGO and INFL. Contribution: This study contributes to the literature on FDI’s impact of FDI on the economic growth of African nations. The research findings have critical policy implications for governments aiming to achieve sustainable economic growth. Practical Implications: This study has policy implications for developmental governance in Nigeria and SSA countries. Novelty: This study deviates from prior studies that agree that illicit financial flows in the form of FDI outflows negatively affect growth and focus on FDI net inflows’ beneficial impacts on the Nigerian economy.

The Determinants of Foreign-Direct-Investment (FDI) Inflows in Nigeria

ABSTRACT: Due to the importance of foreign direct investment (FDI), scholars are always keen to explore FDI determinants and analyze their implications. Nevertheless scholars have proposed mixed viewpoints of FDI and interpreted its determinants differently, which do not contribute to the knowledge advancement and amalgamation of FDI literatures. The current research, therefore, aims to advance the knowledge of FDI determinants in Nigeria through a new investigation on the key determinant factors affecting Nigeria's inward FDI. Data were collected from UNCTAD (1970-2014) and analyzed by auto-regressive distributed lag tests (ARDL). A comprehensive theory-based model was developed accounting for many variables, such as the interest rate, external debt, oil rents, the Gross Domestic Product (GDP) growth rate, trade and exchange rate volatility. The analysis of FDI determinants in the Nigerian economy yielded reliable, robust, and economically meaningful results thereby offering an insight into the driving factors of inward FDI. Findings indicate that the interest rate, external debt, oil rents, and GDP growth are all important determinants, possessing a long-run effect on FDI. Different from the literature, however, trade and exchange rate volatility are barely important to FDI. Several policy implications flow from the findings. From a policy point of view, regarding the GDP growth rate, there should be concerted efforts to boost the performance of the non-oil sector in Nigeria through more investments in the agricultural and industrial sectors making the growth of the economy spread across other sectors and, in turn, encouraging inward FDI in such areas. Countries such as Nigeria, endowed with natural resources, should pursue policies targeted at full deregulation (privatisation) of their natural resource sector to better utilize the abundance of their natural resources thereby attracting additional FDI. Nigeria should also pursue better debt management practices. When debts are acquired, they should be targeted towards future consumption and longer-term investments. Most importantly, as an import-dependent economy, the Nigerian government should also formulate export-driven and appropriate fiscal policies that will stabilize Nigeria's trade relationship with other world economies. The Nigerian government should create the necessary environment that will regulate macroeconomic and specifically monetary policy (interest rate) which is essential for the attraction of FDI inflows into the economy. Finally, Nigeria should ensure that the quality of exportable commodities is improved to enhance international competitiveness.

The insignificance of GDP growth rate to people’s welfare: a political economy analysis of China’s impressive GDP

In recent decades, there was a fierce debate about whether Gross Domestic Product (GDP) accurately describe people’s welfare. This manuscript aims to prove, an impressive GDP does not mean that people live on welfare. This topic is important to be discussed, simply, because it shows the actual condition of the people during their impressive GDP growth, besides, theoretically, it also sheds light on the correlation between GDP and real welfare. Taking China between 1980-2014, when China experienced the highest GDP growth as a case study, this investigation used secondary data provided by previous empirical research documents and international organizations’ data such as UNDP, WHO, World Bank and other relevant sources. The data was analyzed by comparative research method for comparing China’s GDP rate with China’s achievement on five aspects of alternative economic development gauges: inequality rates, life expectancy, health quality, education attainment, and environmental impacts. This investigation figured out that the impressive of China’s GDP has no direct correlation to Chinese real welfare. Along with the impression of China’s GDP, the Chinese must deal with inequality, the low government budget for human resources development, and environmental issues. This finding might support the previous analysts who were pessimists about the accuracy of GDP in measuring people’s welfare and develop a new path for future research. 

International price earnings and country risk model in an Asian context

PurposeThis paper aims to estimate a statistical model of the country risk determination as represented by the country price earnings ratio (PE) to identify potentially mispriced countries. It uses the gross domestic product (GDP) growth rate and a dummy indicator for market-related events (i.e. financial crises), both approximating the business cycle. The model is used to compare a major Asian country’s (i.e. Japan) risk with Western countries’ risk.Design/methodology/approachThe model used finance variables such as the systemic, non-diversifiable, risk and foreign direct investments to characterize any country risk. A random effects model with panel data estimated the effects of macroeconomic and financial variables on PE. The simultaneity problem was checked using two stage least squares and some lagged independent variables.FindingsThe results explained to investors the country risk contributing factors: PE was positively correlated with variables that may increase dividends and market risk premia similar to GDP growth rates and total risk and negatively correlated with variables that increase market risk, namely, nominal risk-free interest rates and financial crises. Japan’s PE seemed to exceed most of the Western countries considered here, implying lower risks, lower interest rates and higher growth in the major Asian country Japan.Originality/valueThis paper focuses on the effectiveness of country risk measures in predicting periods of intense instability, similar to financial crises. This study contributes a model to measure market risk premium, using PE (or inversely, the earnings yield) as a proxy variable. Investors can use this risk measure in picking less risky stocks to include in their portfolio, calling for liberalizing Asian countries’ financial markets to improve their stock market capitalization.

Analysis and prediction of the incidence and prevalence trends of gonorrhea in China

ABSTRACT To understand the epidemiological trend of gonorrhea in China from 2004 to 2021, predict the prevalence of the disease, and provide basic theory and data support for monitoring and managing gonorrhea. Gonorrhea incidence data in China from 2004 to 2021 were collected through the China Public Health Science Data Center and National Administration of Disease Prevention and Control, and the incidence and epidemiological characteristics were analyzed. Statistical analysis was performed using Joinpoint and autoregressive integrated moving average (ARIMA) models. A linear correlation model was used to analyze the correlation between gross domestic product (GDP) and the incidence rate. From 2004 to 2021, a total of 2,289,435 cases of gonorrhea were reported in China, with an average reported incidence rate of 9.46/100,000 people and a downward followed by an upward trend. Individuals with gonorrhea were primarily 20–30 y of age, with 1,034,847 cases (53.38%) from 2004 to 2018. The trend of increasing incidence was most obvious in the 10–20 age group (5,811 cases in 2004 to 12,752 cases in 2018, AAPC = 6.1, P < .001). The incidence of gonorrhea in China was negatively correlated with GDP from 2004 to 2021 (r = -0.547, P = .019). The correlation coefficient between the average incidence growth rate of each region from 2012 to 2018 and the average growth rate of regional GDP was 0.673 (P < .01). The root mean square error (RMSE) of the ARIMA model was 4.89%, showing powerful performance. There would be 97,910 gonorrhea cases in 2023 as predicted by the model.

Powering Ghana’s future: unraveling the dynamics of electricity generation and the path to sustainable energy

This study investigates powering Ghana’s future: unraveling the dynamics of electricity generation and the path to sustainable energy by estimating endogenous parameters and employing an unrestricted Vector Autoregression (VAR) model. The model examines the linear lead–lag relationships between variables in the Ghanaian electricity sector and power consumption, using data from 2002 to 2021. The results reveal structural long-and-short-run headwinds for the unrestricted models and indicate that the growth rate of the Gross Domestic Product (GDP) and electricity from fossil fuels are directly correlated. Granger causality analysis highlights a feedback relationship between GDP growth rate and electricity from fossil fuel sources. The impulse response function reveals that the GDP growth rate is sensitive to exogenous shocks with lasting effects. Variance decomposition results show that renewable energy without hydropower explains a minimal variance due to shocks, while total global greenhouse emissions account for a significant proportion of the variance due to headwinds. Electricity from fossil fuel sources explains a substantial part of the variance due to headwinds, suggesting Ghana’s overreliance on conventional energy sources. The study forecasts that installed renewable energy capacity will experience considerable growth by 2036, accounting for most of the energy mix. To promote a sustainable energy future, the study recommends implementing fiscal instruments that incentivize renewable energy consumption, gradually diversifying the energy mix towards natural gas as a medium-term transition fuel for grid electricity generation and shifting entirely to renewables in the long time. This research contributes valuable insights into the dynamics of electricity generation in Ghana and provides policy recommendations for sustainable energy development.

Food insecurity indicators of 14 OECD countries in a health economics aspect: A comparative analysis.

Food insecurity is a critical issue that refers to a lack of access to adequate food to support a healthy and active lifestyle. This problem has wide-reaching effects and can negatively impact health, education, and overall well-being. Addressing food insecurity requires a multifaceted approach that involves the efforts of governments, organizations, and individuals to ensure access to a balanced and nutritious diet for all. The aim of this study is to shed light on macro-level models and evaluate food insecurity risk in international comparisons. We considered six criteria to evaluate food insecurity risk in terms of health expenditure, gross domestic product (GDP) per capita, and GDP growth rate among 14 Organisation for Economic Co-operation and Development (OECD) countries. We developed a modeling approach in three stages to compare food insecurity risk and discussed the reasons for the rankings of the countries based on the model results. According to our findings, the United States has the lowest food insecurity risk, while Colombia has the highest. The results suggest that economic factors, such as GDP per capita and GDP growth rate, play a significant role in food insecurity risk. The study highlights the importance of addressing economic disparities and promoting economic growth to reduce food insecurity. This study provides insights into the relationship between food insecurity and economic factors, indicating that addressing economic disparities and promoting economic growth can reduce food insecurity. Future research using similar models to link economic outcomes with important health components such as nutrition and physical activity could provide a foundation for policy development.

Impact of Corruption on Economic Growth in Pakistan: An Analysis

The main objective of the study is to examine the effect of corruption on economic development over the period of 1996-2021.The time series analysis of data is taken from transparency international and world development indicator (WDI). The dependent variable utilized in the study is gross domestic product (GDP) Corruption, Inflation and Trade openness has been used as independent variable. The first applied the unit root test and the result shows that all variables are integrated at the first difference. Johansen cointegration test technique is applied for cointegration and vector error correction model (VECM) applied for long run and short run analysis in the case of Pakistan. There exist long run relationship among the variables and short run relationship is not exist. Empirically outcomes show that trade-openness has a positive effect on growth. Inflation and the corruption has the negative effect on growth rate of GDP.

Does Fiscal Deficit Cause Economic Growth? Evidence from Nepal

The fiscal deficit soared tremendously after Nepal endorsed economic liberalization aftermath of the 1990s democracy. The purpose of the paper is to investigate the effects of fiscal deficit in the economic growth of Nepal. Data from 1980 to 2019 were used to estimate the short- and long-run causal relations of fiscal deficit proxied by total government expenditure minus total government revenue as a percent of gross domestic product (GDP) and growth rate of real GDP as a proxy of economic growth along with other control variables. The autoregressive distributed lag (ARDL) error correction regression and vector error correction model (VECM) Granger causality have employed to meet the purpose of the study. The study reveals that there is short-run statistically significant negative relationship between Nepal's economic growth and the fiscal deficit of the previous year. However, both domestic and foreign loan and economic growth have positive and significant relations in short run. In contrast, the results reveal that there is positive but not significant relationship between the fiscal deficit and the economic growth of Nepal in long run. The Granger causality suggests that fiscal deficit does not Granger cause the economic growth of Nepal. However, economic growth does Granger causes the fiscal deficit in Nepal. Thus, the growth-driven fiscal deficit will be more effective in the economy of Nepal. Based on stylized facts, it will be necessary to redirect deficit financing into productive channels in order to enhance the sustainable economic growth.

Quantitative assessment of the response of seawater environmental quality to marine protection policies under regional economic development --A case study of Xiamen Bay, China

Xiamen is the epitome of having steady economic growth and non-negligible environmental stress over decades. Several restoration programs have been applied to address the conflicts between heavy environmental pressures and human activities, but the response of current coastal protection policies to the marine environment remains to be assessed. Therefore, to assess the effectiveness and efficiency of marine conservation policies under regional economic growth in Xiamen, quantitative techniques including elasticity analysis and dummy variable regression models were applied. Here we show the potential relationship between seawater quality (pH, COD, DIN and DRP) and economic growth including Gross Domestic Product (GDP) and Gross Ocean Product (GOP), to evaluate the ongoing related policies by using over 10 years of data (2007–2018). According to our estimates, a GDP growth rate of 8.5% represents a stable economic climate that is favorable for the overall rehabilitation of the local coastal environment. The results of the quantitative research indicate a strong relationship between economic development and seawater quality, with marine protection regulations serving as the direct cause. As GDP growth and pH are significantly positively correlated (coef. = 0.8139, p = 0.012), ocean acidification has decreased over the last decade. With an inversely proportional correlation with GDP (coef. = 0.8456, p = 0.002) and GOP (coef. = 0.8046, p = 0.005), the trend in COD concentrations effectively meets the targets of current pollution control legislation. By using a dummy variable regression model, we found that legislation is the most effective way in seawater recovery in the GOP section, and positive externalities of marine protection frameworks are also estimated. Meanwhile, it is predicted that the negative effects from the non-GOP section will gradually affect the coastal environmental quality gradually. An overall framework for controlling marine pollutant discharges, giving equal attention to maritime and non-maritime anthropogenic activities should be promoted and updated.