Growth Intensity Research Articles

Analysis on the changes of fertilization intensity and efficiency in China's grain production from 1980 to 2019.

Relieving serious non-point source pollution of nitrogen (N), phosphorus (P), and potassium (K) is an urgent task in China. It is necessary to explore the changing characteristics of chemical fertilization intensity (FI) and efficiency to provide references. A new method of 'relative productivity proportion weight', which was simpler than data envelope analysis, was proposed to construct models of fertilizer allocation efficiency (FAE) and chemical fertilizer integrated efficiency (FIE) by considering NPK multi-inputs and the grain output scale, respectively. During 1980-2014, the FIs of NPK chemical fertilizers in China showed a significant growing trend. After reaching the highest value of 339 kg ha-1 in 2014, FIs were reduced to 303 kg ha-1 in 2019, higher than the 225 kg ha-1 maximum safe usage internationally recognized. Meanwhile, the pattern of change of FAE was one of 'decreasing to increasing', with values of 1 in 1980, 0.66 in 2003, and 0.80 in 2019. FIE basically showed an increasing trend, which could be divided into three stages: the first stage of low efficiency during 1980-2009, the second stage of medium efficiency after 2010, and the third stage of high efficiency after 2018. From 1980 until 2019, a reduction of FAE from 1 to 0.80 with an average of 0.75 was observed in China. FIE was found between 0.65 and 0.85 and had the potential of upgrading by 15-35%. Therefore, China needs to improve the fertilizer use efficiency in order to strive for negative growth of chemical fertilizer intensity and ecological agriculture construction. © 2022 Society of Chemical Industry.

A simulation experiment on ICT and patent intensity in South Africa: An application of the novel dynamic ARDL machine learning model

The aim of this study is to examine the effect of shocks to patent intensity and its empirical and practical policy implications for the South African economy. This stems from the gap in the literature on policy simulation exercises related to the boost in Information and Communications Technology (ICT) and patent intensity in African countries. Hence, this study established the dynamic relationship between patent intensity and economic growth in South Africa for the period of 1980–2020, alongside essential macroeconomic variables such as government expenditure, gross fixed capital formation, labour force, and trade. We use the Autoregressive distributed lag model (ARDL) to capture short-run and long-run relationships, novel dynamic ARDL and Kernel-based Regularized Least Squares (KRLS) to capture the counterfactual shocks in the economic growth. The ARDL result revealed that government expenditure, labour force, and trade openness significantly foster economic growth in the long-run and short-run. Also, while patent intensity and gross fixed capital formation increase the economy in the long-run and short-run, their interaction term significantly diminishes the growth. Further in the analysis is the dynamic ARDL simulation and KRLS, which predicted the counterfactual shocks of economic growth based on a + 26 % change in patent intensity. The result showed that the increasing volume of patent intensity first has a low effect on South Africa economic growth, but later rebound upwardly, thus indicating that change in patent intensity has a long-lasting impact on sustainable economic growth. The direction that is useful for policy is also highlighted and discussed.

Neural signatures of auditory hypersensitivity following acoustic trauma.

Neurons in sensory cortex exhibit a remarkable capacity to maintain stable firing rates despite large fluctuations in afferent activity levels. However, sudden peripheral deafferentation in adulthood can trigger an excessive, non-homeostatic cortical compensatory response that may underlie perceptual disorders including sensory hypersensitivity, phantom limb pain, and tinnitus. Here, we show that mice with noise-induced damage of the high-frequency cochlear base were behaviorally hypersensitive to spared mid-frequency tones and to direct optogenetic stimulation of auditory thalamocortical neurons. Chronic two-photon calcium imaging from ACtx pyramidal neurons (PyrNs) revealed an initial stage of spatially diffuse hyperactivity, hyper-correlation, and auditory hyperresponsivity that consolidated around deafferented map regions three or more days after acoustic trauma. Deafferented PyrN ensembles also displayed hypersensitive decoding of spared mid-frequency tones that mirrored behavioral hypersensitivity, suggesting that non-homeostatic regulation of cortical sound intensity coding following sensorineural loss may be an underlying source of auditory hypersensitivity. Excess cortical response gain after acoustic trauma was expressed heterogeneously among individual PyrNs, yet 40% of this variability could be accounted for by each cell's baseline response properties prior to acoustic trauma. PyrNs with initially high spontaneous activity and gradual monotonic intensity growth functions were more likely to exhibit non-homeostatic excess gain after acoustic trauma. This suggests that while cortical gain changes are triggered by reduced bottom-up afferent input, their subsequent stabilization is also shaped by their local circuit milieu, where indicators of reduced inhibition can presage pathological hyperactivity following sensorineural hearing loss.

Education and its impact on renewable energy demand, carbon intensity, and green growth: do digital financial inclusion and environmental policy stringency matter in China.

Various targets need to be accomplished before the dream of sustainable development comes true. Among these targets, increasing renewable energy demand, reducing carbon intensity, and achieving green growth are the most noticeable. Therefore, the present study focuses on capturing digital financial inclusion's impact on renewable energy demand, carbon intensity, and green growth in the presence of ICT trade and environmental policy stringency in China. To empirically estimate the model, we have applied ARDL covering the time span from 1995 to 2020. The results state that the estimated coefficients attached to education, ATMS, and environmental policy stringency are positively significant in the renewable energy and green growth model and negatively significant in the carbon intensity model. From these results, we confer that education, digital financial inclusion, and environmental policy stringency are good for increasing renewable energy demand, reducing carbon intensity, and achieving green growth in China. In the short run, some of the estimates are negative; hence, we conclude that the results are inconclusive. The results recommend some imperative policy suggestions.

Crystal plasticity evaluation of laser peening effects on improving high-cycle fatigue life of Al-Li friction stir welded joints

The strengthening mechanisms behind surface modification processes must be evaluated to optimize surface treatment methods. This study developed a physics-based crystal plasticity finite element framework to evaluate multiple-laser-peening impacts on enhancing the high-cycle fatigue properties of AA2195-T6 friction stir welded joints. For this purpose, different contributions of laser peening effects were quantified on the variations of fatigue indicator parameters in the joint region. Extreme-value fatigue indicator parameters evaluated the laser peening effects in decreasing the driving force for fatigue crack initiation at near-surface grain boundaries. Moreover, grain-average fatigue indicator parameters investigated the fatigue property modification trends with depth. Experimental high-cycle fatigue tests were applied to validate the related modeling outputs. According to the crystal plasticity model, the improvement in joint near-surface fatigue properties majorly stemmed from cyclic mean stress alleviation under laser-peening-induced compressive residual stresses. This effect was followed by the increased resistance against cyclic plastic deformation under the growth in dislocation density and crystallographic texture intensity and the reduction in texture heterogeneity under the crystal morphology homogenization effects after laser peening. Fatigue tests revealed the enhancement in the joint fatigue life under the relocation of fatigue crack initiation regions to depth after LP and validated the numerical approaches.

Dynamic relationship between energy efficiency, health expenditure and economic growth: in pursuit for SDGs in Malaysia

PurposeThis paper aims to examine the dynamic relationship among energy efficiency, health expenditure and economic growth in Malaysia over the sample period of 1980–2016.Design/methodology/approachThis study uses autoregressive distributed lag cointegration analysis and the causality approach by the vector error correction model to analyse the relationship among energy efficiency, which is proxied by energy intensity and the determinant factors.FindingsThe findings of this paper suggest long-run cointegration causal links between economic growth and health expenditure. However, a mixed conclusion for both determinants exists: an increase in real income contributes to more efficient use of energy sources, whereas an increase in government spending on health intensifies energy usage.Originality/valueMost previous relevant research has focussed on energy efficiency as measured by economic intensity and economic growth and do not relate to the issue of health expenditure. The recent health catastrophe brought on by the COVID-19 epidemic emphasises the significance of allocating more resources to health care. The findings will be helpful in the development of energy efficiency and economic policies in pursuit of sustainable development goals.

The impact of renewable and non-renewable energy consumption on carbon emission intensity in China: Fresh evidence from novel dynamic ARDL simulations

Given the dire state of global warming, it is critical to investigate the elements that influence carbon emissions intensity and to precisely monitor progress in carbon emissions intensity growth in order to meet the aim of lowering CO2 emissions. This research explores the association among renewable energy and non-renewable energy consumption, the urban population, research and development expenditure, technological innovation, and carbon emissions intensity in China using annual time series data over the period 1990 to 2019. The Dynamic ARDL simulation technique was utilized to investigate the long-run and short-run correlations between renewable and non-renewable energy consumption and CEI. The results suggest that there is strong evidence of a long-run correlation between the variables. The findings indicate that in the long-run, renewable energy and non-renewable energy consumption, and research and development expenditure have a positive influence on CEI by 0.27%, 0.75%, and 0.21%, whereas the urban population has a negative influence by 2.31%, respectively. However, the urban population and technological innovation have positively affected the short-run CEI by 12.17% and 0.23%, respectively. Policies should focus on continuous investment in renewable energy sources, clean energy innovation, improving energy efficiency, forest restoration, and carbon neutrality initiatives to lessen the environmental extreme pressure associated with CO2 emissions.

Spatiotemporal Variation and Influencing Factors of Vegetation Growth in Mining Areas: A Case Study in a Colliery in Northern China

Based on MODIS EVI data of August collected from 2010 to 2021, and taking the Yingpanhao coal mine as an example, the spatiotemporal variation features of vegetation are analyzed using time series analysis, trend analysis and correlation analysis methods in the eco-geo-environment of the phreatic water desert shallows oasis. A significant increase trend is found for vegetation variation, and its development has improved generally in most areas. There is an obvious positive correlation between precipitation and vegetation growth, and a negative correlation between coal mining intensity and vegetation growth, but the influence of atmospheric precipitation on vegetation growth is stronger than that of coal mining intensity in the eco-geo-environment. The research results effectively reflect that atmospheric precipitation is the primary factor advancing the vegetation growth status in the coal mining regions. Vegetation development response to coal mining would be degraded first, then improved, and finally restored in areas with a deeply buried phreatic water level; that would promote the transformation of vegetation species from hydrophilous plants to xerophyte plants in areas with a shallowly buried phreatic water level. Therefore, it is necessary to carry out reasonable mine field planning according to the phreatic water level and the vegetation type distribution and to adopt different coal mining methods or corresponding engineering and technical measures to realize water conservation to avoid damaging the original hydrogeological conditions as far as possible. This information is helpful for promoting the eco-geo-environmental protection and further establishing the need for the dynamic monitoring of the eco-environment in the coal mining regions in the arid and semi-arid ecologically vulnerable areas of Northern China, which play a significant role in the long-term protection and rehabilitation of the eco-geo-environment and in the promotion of sustainable development.

When Does AI Pay Off? AI-Adoption Intensity, Complementary Investments, and R&D Strategy

This paper examines how high-tech venture performance varies with AI-adoption intensity. We find that firm revenue increases only after sufficient investment in AI, and the benefits of AI adoption are greater at firms that also invest in complementary technologies and pursue internal R&D strategy. Specifically, AI adoption at low levels does not suggest significant revenue growth, but, as the intensity of AI adoption increases revenue growth occurs. We find that such performance gains from adoption are larger among firms that invest in complementary technologies such as cloud computing and database systems. Moreover, the positive relationship between AI adoption intensity and revenue growth is stronger among firms that pursue a more exclusive R&D strategy specific to the venture.

Have those countries declaring “zero carbon” or “carbon neutral” climate goals achieved carbon emissions-economic growth decoupling?

Protecting the ecological environment is the common task of all mankind, and it is difficult for any country to achieve its goals alone. As matters stand, more than 130 countries and regions have proposed “zero carbon” or “carbon neutral” climate goals. This study established a three-stage carbon emission analysis framework for 29 countries that have proposed carbon neutrality targets. First, the carbon emissions of each country are decomposed based on Logarithmic Mean Divisia Index (LMDI) method. Then, the decoupling relationship between carbon emissions and economic development of each country is analyzed based on the Tapio model. Finally, according to the current and historical decoupling status, the future carbon emission trends of various countries are studied. The results indicated: (1) Energy intensity and economic growth are key drivers of carbon emissions. (2) There are certain differences in the decoupling status of countries during each period. Of which, the United States performed best, followed by the four countries whose climate goal process is in law (France, the United Kingdom, Hungary and New Zealand). (3) No matter in current decoupling state scenario or historical decoupling state scenario, the emission reduction effects of various countries are not satisfactory. Few countries can successfully achieve NDC targets or carbon neutral goals.

Energy efficiency in residential and non-residential buildings: Short-term macroeconomic implications

Investments in energy efficiency are vital for reducing greenhouse gas emissions by 2030 given that changes in the structure of the main energy sources cannot be expected in the short to medium term. The greatest potential for energy savings and cutting emissions lies in buildings due to their significant carbon intensity and rapid growth rate. However, gaps remain in what is known about integrating decarbonisation of the built environment into the economy-wide system. The paper addresses such gaps by examining the socio-macroeconomic implications of different scenarios related to building stock investments in a small open economy. The effectiveness of the measures implemented for various building types and the possibility of a rebound effect are also considered. The study is valuable for the peculiarities of the recursive dynamic computable general equilibrium model, GreenMod Slovenia, which was established to analyse the effects of decarbonisation. Two major contributions emerge: (1) projected energy efficiency in buildings is presented separately for commercial services, public services and households in two investment scenarios, namely, the business-as-usual scenario and the energy efficiency scenario with additional investments; and (2) the disaggregation of households into income quintiles to project their consumption of energy inputs. The study reveals vital macroeconomic benefits flowing from the energy efficiency scenario, including higher GDP and employment. Second, energy efficiency improvements in commercial services might encourage higher energy consumption. Finally, low-income households reduced the consumption of energy products the least. In this quintile group, energy efficiency improvement can lead to greater energy consumption, denoting policy failure.

CO2 emissions in the Middle East: Decoupling and decomposition analysis of carbon emissions, and projection of its future trajectory

Despite their acute climatic susceptibility, the hot, arid Middle Eastern countries are among the world's largest energy consumers and emitters of greenhouse gases, particularly carbon dioxide (CO2). Nonetheless, no study has been conducted to decompose regionally the influential primary factors of the Middle East's carbon emissions. This study utilized the logarithmic mean Divisia index (LMDI) method to fill this knowledge gap and investigate the driving forces of CO2 emissions in 12 Middle Eastern countries, namely, Bahrain, Iran, Iraq, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Syria, the United Arab Emirates, and Yemen (1990–2020). The research confirmed that, with a contribution rate of 53.89 %, population growth is the primary driver of CO2 emissions in the Middle East, followed by energy intensity (31.97 %) and economic growth (18.42 %); and the most straightforward approach to reduce emissions, are boosting energy efficiency and reforming energy subsidies. It also concluded that the West Asian economy is gradually decoupling from CO2 due to the effective decarbonization of countries, such as Saudi Arabia and Kuwait, based on the Tapio decoupling model results. Furthermore, each country's future emissions (2020–2026) were projected using a novel group method of data handling (GMDH) approach based on the main identified factors. The countries' decoupling status confirms the accuracy of the projected data on CO2 emissions growth. The region's CO2 emissions are expected to rise 13.28 % by 2026, with Syria and Yemen experiencing the most significant increases (129.45 % and 112.14 %, respectively) due to post-civil war economic growth. Other aspects of regional conflicts and migration impacts on the CO2 emission influencing factors were also explored. Indeed, besides providing a comprehensive analysis of the current and future status of CO2 emissions in the Middle East, the effects of military conflicts on CO2 emissions have been investigated using this regional case study for global application.

Investigating the role of economic complexity in sustainable development and environmental sustainability

ABSTRACT A productive economic structure, represented by economic complexity (ECP), augments the economic development of nations. However, previous empirical investigations on this topic are confined to understanding the direct effects of economic complexity on environmental quality with equivocal outcomes. Therefore, this study not only probed the effects of ECP on the ecological footprint (EF) but also explored the indirect environmental effects of ECP through the channel of economic growth. To do so, the study employed the unit-root tests with structural breaks and without structural breaks. Afterward, the newly developed Augmented Autoregressive Distributed Lag (AARDL) method was used on the time series Indian data from 1970 to 2017 in the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) framework. The findings disclosed cointegration among the selected variables. Notably, the AARDL test provided a reliable conclusion by using three different tests compared to the conventional ARDL model that relies on just one F-statistics. The long-run empirical results unfolded that ECP not only reduces footprint but also decreases the adverse environmental impacts of economic growth. In addition, energy intensity and economic growth contribute to escalating the EF, and thereby, increase environmental degradation. Nevertheless, population density decreases the footprint, and thus, stimulates ecological sustainability. Besides, ECP Granger causes EF. After this, several diagnostic tests were performed to confirm the stability of the models and the results were verified using the Fully Modified Ordinary Least Squares test. Lastly, the study directed policies to increase the level of sophisticated knowledge-based production for sustainable growth.

When does AI pay off? AI-adoption intensity, complementary investments, and R&D strategy

This paper examines how high-tech venture performance varies with AI-adoption intensity. We find that firm revenue increases only after sufficient investment in AI, and the benefits of AI adoption are greater at firms that also invest in complementary technologies and pursue internal R&D strategy. Specifically, AI adoption at low levels does not suggest significant revenue growth, but, as the intensity of AI adoption increases revenue growth occurs. We find that such performance gains from adoption is larger among firms that invest in complementary technologies such as cloud computing and database systems. Moreover, the positive relationship between AI adoption intensity and revenue growth is stronger among firms that pursue a more exclusive R&D strategy specific to the venture.

ICT, Energy Intensity, and CO2 Emission Nexus

The relationship between information and communication technology investment (ICT), environmental impacts, and economic growth has received increasing attention in the last 20 years. However, the relationship between ICT, energy intensity, environmental impacts, and economic growth was relatively neglected. In this paper, we aimed to contribute to the environmental literature by simultaneously analyzing the relationship between ICT, energy intensity, economic growth, Carbon dioxide (CO2) emissions, and energy consumption for the period of 1990–2020 in G7 countries. We employed the Panel Quantile Auto Regressive Distributed Lag (PQARDL) method and Panel Quantile Granger Causality (PQGC) methods. According to the results of PQARDL method, energy consumption, ICT, CO2 emission, and energy intensity have effects on economic growth in the long and short run. According to the of PQGC methods allowing causality results for different quantiles, there is evidence of a bidirectional causality between ICT investment and economic growth for all quantiles and evidence of a unidirectional causality from ICT to energy consumption and from CO2 emissions to ICT investment and energy efficiency. Our results indicate that the governments of the G7 countries have placed energy efficiency and ICT investment at the center of their policies while determining their environmental and energy policies, since energy consumption is a continuous process.

A study of the oxidative processes in human plasma by time-resolved fluorescence spectroscopy

The aim of this study was to examine the usefulness of time-resolved fluorescence spectroscopy in the evaluation of the oxidative processes in human plasma. To investigate the impact of oxidative stress on the fluorescence of plasma, five studied markers (thiobarbituric acid-reactive substances, ischemia modified albumin, carbonyl groups, hydrogen peroxide, advanced oxidation protein products) were chosen as oxidative damage approved markers. Our method presents several advantages over traditional methods as it is a direct, non-time-consuming, repeatable, and non-invasive technique that requires only simple pre-treatment of samples without additional reagents and the sample size needed for analysis is small. In principle, each modification of the protein in plasma can be expected to modify its fluorescence properties and hence its lifetime or intensity. The study involved 59 blood donors with no evidence of disease. The research was conducted at excitation wavelengths of 280 nm and 360 nm, and emission was measured at wavelengths of 350 nm and 440 nm, respectively. Our results, although preliminary, suggest that the application of fluorescence measurements can be considered as an effective marker of oxidative stress. Regression analyses showed that a notable growth in fluorescence intensity at 440 nm and a simultaneous decrease in fluorescence intensity and mean fluorescence lifetime at 350 nm are associated with higher levels of oxidative stress.

Exogenous melatonin stimulated isoflavone biosynthesis in NaCl-stressed germinating soybean (Glycine max L.)

Melatonin (MT) has gained increasing attention due to its pleiotropic effects. In this study, the function of exogenous MT on the response to NaCl stress and isoflavone biosynthesis in germinating soybeans was investigated. Results showed the exogenous MT (100 μM) application neutralised the negative effects of NaCl stress (60 mM), induced sprout growth, biomass and fluorescence intensity of intracellular free calcium, decreased malondialdehyde, H2O2 content and fluorescence intensity of O2•-, and enhanced superoxide dismutase, catalase and peroxidas activities of germinating soybeans. Meanwhile, total flavonoids and different forms of isoflavone content were enhanced by MT application, not only companied by the up-regulated relative gene expression of cinnamic acid 4-hydroxylase chalcone reductase, chalcone isomerase 1A, isoflavone reductase and isoflavone synthase 1 that involved in isoflavone biosynthesis, but also increased activities of phenylalanine ammonia lyase and 4-coumarate coenzyme A ligase. Given the evidence from the present study, it's proposed that the exogenous MT could relieve NaCl stress and stimulate isoflavone biosynthesis in germinating soybeans.

Dynamic Evolution Characteristics of Oil–Gas Coupling Fractures and Dynamic Disaster of Coal Mass in Coal and Oil Resources Co-Storage Areas

The dynamic evolution characteristics of fracture are crucial to analyzing the development of coal and rock mass instability, gas diffusion law, and dynamic disaster prediction. These characteristics affect coal rock fractures and gas-induced disaster channels. Based on the experimental results, we found the following: In areas rich in both coal and oil, the coal mass damage due to high-pressure oil diffusion was inversely correlated with the distance of the coal mass from the disaster source. Moreover, as the coal mass became closer to the disaster source in the abandoned oil well, the average CT number reduced, mechanical performance diminished, fracture volume ratio increased, and capacity of the oil and gas storage improved. The four-dimensional analysis of the rock evolution and strength grade showed that coal mass stress, fracture propagation, and AE incidents were characterized by a low-strength compression stage featured by tensile fractures, medium-strength elastic stage with shear fractures replacing tensile fractures, and high-strength rapid fracture stage with rapid growth in shear fracture quantity and intensity. The dynamic conversion between coal mass instability disaster in coal and oil resources co-storage areas and oil and gas disaster was clarified. We propose high strength, high RA, and low AF of AE incidents in the rapid fracturing stage as the qualitative warning factors for the fracture of coal mass and the occurrence of oil and gas disaster.

Entrepreneurial marketing and firm performance: scale development, validation, and empirical test

ABSTRACT This research introduces a new scale (ENMAR) for measuring entrepreneurial marketing (EM). The interrelationships between EM, market orientation (MO), entrepreneurial orientation (EO), firm performance, and the moderating effects of network structure (i.e. size, diversity, and strength), environmental variables (i.e. market turbulence, technological turbulence, competitive intensity, supplier power, and market growth), and firm size are empirically examined. Using structural equation modeling, data from 401 U.S. based firms representing a broad spectrum of industries and firm sizes are analyzed. Empirical findings demonstrate that even after controlling for MO and EO, EM has a positive and significant impact on firm performance, and that impact becomes even more pronounced under highly uncertain market conditions. EM partially mediates the well-established relationships between MO, EO, and firm performance. While EM robustly boosts firm performance, it is more frequently practiced by young firms and those in B2B markets, though it may be particularly beneficial for mid-sized firms.

The Effect of Variable Light Source and Light Intensity on the Growth of Three Algal Species.

Light is the essential energy source for autotrophically growing organisms, including microalgae. Both light intensity and light quality affect cell growth and biomass composition. Here we used three green algae—Chlamydomonas reinhardtii, Desmodesmus quadricauda, and Parachlorella kessleri—to study the effects of different light intensities and light spectra on their growth. Cultures were grown at three different light intensities (100, 250, and 500 µmol m−2 s−1) and three different light sources: fluorescent lamps, RGB LEDs, and white LEDs. Cultures of Desmodesmus quadricauda and Parachlorella kessleri were saturated at 250 µmol m−2 s−1, and further increasing the light intensity did not improve their growth. Chlamydomonas reinhardtii cultures did not reach saturation under the conditions used. All species usually divide into more than two daughter cells by a mechanism called multiple fission. Increasing light intensity resulted in an increase in maximum cell size and division into more daughter cells. In Parachlorella kessleri cells, the concentration of photosynthetic pigments decreased with light intensity. Different light sources had no effect on algal growth or photosynthetic pigments. The results show a species-specific response of algae to light intensity and support the use of any white light source for their cultivation without negative effects on growth.