Output Of Manufacturing Industries Research Articles

Ecological Consequences of Changes in the Territorial and Branch Structure of Moscow Manufacturing Industry in 1990–2023

The peculiarities of the dynamics of the volume and structure of emissions from manufacturing enterprises in Moscow in 1990–2023 have been revealed. It is noted that the economic process of deindustrialization in the 1990s–2000s has been replaced in recent years by a small reverse process of reindustrialization, with an increase in the diversity of sectoral structure, and a change in the role and internal structure of machine building and chemical industry. The decrease of manufacturing industry output was noticed, while pollution from large enterprises producing products necessary for the city was preserved; at the same time, the rates of decrease in specific pollution for these enterprises have slowed down in recent years. It is concluded that during periods of crisis and recession there is a faster change in industrial structure compared to the change in pollution structure, and that during the recovery phase the pollution structure changes faster, largely due to the fact that industries are expanded initially without adequate expansion of treatment systems.

Acid Concentration: A Pre-requisite to Enhanced Metal Dissolution?

Technological advancement over the past decade has dramatically changed with new players such as China taking lead in information-technology and telecommunication market across the world. This development has seen surge in manufacturing industry output and consequently raw materials consumed by the sector. The environmental impact is however evident due to human interference with nature. E-waste is the fastest growing waste stream posing serious risk to fauna and flora due to numerous associated pollutants. In recycling industry, hydrometallurgy is widely adopted to recover metals from e-waste. Despite growing concern over secondary pollution associated with disposal, concentrated aqua regia is widely used to extract metals from e-waste. An ultrasonic bath enhanced dilute-acid leaching method was developed to recover copper and lead from waste mobile phone PCB. Repeatability and reproducibility showed 5.3% and 5.0% with respect to Cu while 9.8% and 6.3% RSD were recorded for lead. LOD and LOQ of 0.02 µg/g and 0.064 µg/g (Cu) and 0.082 µg/g and 0.312 µg/g (Pb) revealed that the method is suitable for dissolution of target metals from PCBs. Copper concentration of 332.98 mg/g recorded inferred that dilute aqua regia as much as concentrated has promising metal extraction power and thus could replace concentrated acids.

Empirical Evidence of a Long-Run Relationship Between Agriculture and Manufacturing Industry Output in Nigeria

This study investigates a long-run relationship between agriculture and manufacturing industry output in Nigeria using annual time series data from 1982 to 2017. The study employs Granger causality test, vector error correction model, and co-integration techniques to estimate the interdependence between agricultural productivity and manufacturing industry output. Empirical evidence from Granger causality test reveals a bidirectional relationship between agricultural productivity and manufacturing industry output. Although a positive and significant relationship exists in the short- and long-run estimates, a long-run divergence from the vector error correction model indicates that changes in agricultural productivity are not restored to equilibrium, given that macroeconomic factors distort the linkage. Policy implications suggest that macroeconomic stability is a necessary condition for agriculture and manufacturing industry output to foster economic growth.

Modeling the transformation of the impact of production factors on the economy in the process of smart industry formation

Идентифицирован фактор производства, связанный со смарт-индустриализацией, и на примере перерабатывающей промышленности Германии как страны, в которой на государственном уровне провозглашена и реализуется программа развития«Промышленность 4.0», выполнено моделирование соответствующей производственной функции.Аргументировано, что существующие подходы к учету научно-технического прогресса при построении производственных функций не подходят для моделирования трансформации влияния производственных факторов в процессе становления смарт-промышленности, поскольку научно-технический прогресс обычно представлен не конкретным измеримым показателем, а просто натуральным рядом чисел, отражающим ту часть изменения производства, которая не объясняется изменением учитываемых факторов.Установлено, что в перерабатывающей промышленности Германии в условиях снижения затрат труда и капитала выпуск продукции растет. Это свидетельствует о влиянии еще как минимум одного фактора, связанного с переходом к новому технологическому укладу - смарт-промышленности. Идентифицированы сложности оценки влияния смарт-фактора на производство - как объективные (взаимозависимость факторов информатизации и сложность выделения вклада каждого из них), так и субъективные (полное отсутствие или фрагментарность статистической информации). На основе анализа статистики установлено, что наиболее точным показателем, отражающим влияние на производство фактора информатизации, выступает стоимость программного обеспечения и баз данных (ПО и БД).Построена модель, представляющая собой адаптацию модели Кобба-Дугласа, в которой в качестве эндогенной переменной используется добавленная стоимость в перерабатывающей промышленности, а в качестве экзогенных - количество отработанных часов (фактор труда), стоимость машин и оборудования с лагом в 1 год (фактор капитала) и стоимость ПО и БД (фактор информатизации). Анализ результатов моделирования позволил сделать вывод о том, что информатизация превратилась в важный фактор производства и демонстрирует потенциал к замещению прочих факторов производства - труда и капитала. Модель может использоваться для обоснования направлений развития смарт-промышленности на макроуровне, а также может быть положена в основу разработки критериев оценки уровня «смартизации» предприятий.

Evidence of Long-Run Relationship between Agriculture and Manufacturing Industry Output in Nigeria

This study investigates a long-run relationship between agriculture and manufacturing industry output in Nigeria using a time series data from 1982 to 2015. The study employs the Granger causality test, Vector Error Correction Model and co-integration technique to estimate the interdependence between agricultural productivity and manufacturing industry output in the light of selected macroeconomic variables. Empirical evidence from Granger Causality test reveals a bidirectional relationship between agricultural productivity and manufacturing industry output. Although, a positive relationship exists between agriculture and manufacturing industry output in the short and long-run estimates, a long-run divergence from the vector error correction model indicates that variations in agricultural productivity are not restored to equilibrium given the behavior of explanatory variables, which implies that macroeconomic conditions distort the linkage. A major policy implication is that developing countries need to have a measure of macroeconomic stability for a strong agriculture and manufacturing industry linkage to foster sustainable economic development.

An analysis of two leading indicators of economic growth in Turkey: Monthly manufacturing industry output and CNBC-e consumption indices

Bu çalışma aylık olarak yayınlanan iki temel göstergenin ekonomik büyümeyi önceden tahmin etme gücünü test etmeyi amaçlamaktadır. Test edilecek göstergeler aylık CNBC-e Tüketim Endeksi ve İmalat Sanayi Üretim endeksleridir. CNBC-e Tüketim Endeksinin Türkiye’de toplam harcamaların yüzde 70’den fazlasını oluşturan tüketim harcamalarının öncü bir göstergesi olması beklenebilir. Diğer taraftan, Türkiye’deki toplam katma değerin yüzde yirmi beşini oluşturan imalat sanayi toplam üretimin çekirdeği gibidir. Dolayısıyla, imalat sanayi üretiminin GSYİH ile önemli bir korelasyon içinde olması beklenebilir. Bu çalışma, söz konusu iki göstergenin ekonomik büyümeyi erken tahmin etmede ne kadar güvenilir olduğunu test edecektir. Bulgularımız her iki endeksin Türkiye’de ekonomik büyümeyi anlamlı bir şekilde önceden tahmin ettiğine işaret etmektedir.

Lietuvos aprangos ir tekstilės pramonės konkurencingumo stiprinimo galimybės

Straipsnio tikslas - išanalizuoti Lietuvos aprangos ir tekstilės pramonės įmonių konkurencinę būseną, susiformavusią po Lietuvos stojimo į Europos Sąjungą ir tekstilės gaminių importo liberalizavimo, ir pasiūlyti priemones šios pramonės įmonių konkurencingumui stiprinti. Pritaikius M. Porterio “Deimanto” modelį pagal antrinius ir pirminius duomenis buvo atliktas Lietuvos aprangos ir tekstilės pramonės įmonių konkurencingumo dimensijų tyrimas. Jis atskleidė šios Lietuvos pramonės vis dar didelę priklausomybę nuo darbo jėgos kainos, sisteminio požiūrio trūkumą plėtojant šią pramonės šaką bei parodė, kad išnaudojamos ne visos verslo aplinkos teikiamos galimybės.

The Output of Manufacturing Industries 1899-1937

The Output of Manufacturing Industries 1899-1937

The Output of Manufacturing Industries, 1899-1937

The Output of Manufacturing Industries, 1899-1937

The Output of Manufacturing Industries 1899-1937

The Output of Manufacturing Industries 1899-1937