Success.ai’s Fashion & Apparel Data for Apparel, Fashion & Luxury Goods Professionals in Asia provides a robust dataset tailored for businesses seeking to connect with key players in Asia’s thriving fashion and luxury goods industries. Covering roles such as brand managers, designers, retail executives, and supply chain leaders, this dataset includes verified contact details, professional insights, and actionable business data.

With access to over 700 million verified global profiles and 130 million profiles focused on Asia, Success.ai ensures your outreach, marketing, and business development strategies are supported by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution positions you to succeed in Asia’s competitive and ever-growing fashion markets.

Why Choose Success.ai’s Fashion & Apparel Data?

1. Verified Contact Data for Precision Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of professionals in apparel, fashion, and luxury goods industries across Asia.
   • AI-driven validation ensures 99% accuracy, reducing bounce rates and enhancing communication efficiency.

2. Comprehensive Coverage of Asian Fashion Professionals

   • Includes profiles from major fashion hubs such as China, India, Japan, South Korea, and Southeast Asia.
   • Gain insights into regional consumer trends, emerging fashion markets, and luxury goods opportunities.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership, market expansions, and product launches.
   • Stay aligned with evolving industry trends and capitalize on new opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with professionals across the global fashion and apparel industries, with a focus on Asia.
• 130M+ Profiles in Asia: Gain detailed insights into professionals shaping the region’s fashion and luxury goods markets.
• Verified Contact Details: Access work emails, phone numbers, and business locations for precise targeting.
• Leadership Insights: Engage with designers, brand managers, and retail leaders driving Asia’s fashion trends.

Key Features of the Dataset:

1. Comprehensive Professional Profiles

   • Identify and connect with decision-makers in apparel design, luxury goods branding, retail operations, and supply chain management.
   • Target individuals leading innovation in sustainable fashion, fast fashion, and digital transformation.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (luxury goods, ready-to-wear, footwear), geographic location, or job function.
   • Tailor campaigns to align with specific market needs, such as emerging e-commerce platforms or regional fashion preferences.

3. Industry and Regional Insights

   • Leverage data on consumer behaviors, market growth, and regional trends in Asia’s fashion and luxury goods sectors.
   • Refine marketing strategies, product development, and partnership outreach based on actionable insights.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Brand Expansion

   • Design targeted campaigns to promote apparel, luxury goods, or retail solutions to fashion professionals in Asia.
   • Leverage multi-channel outreach, including email, phone, and social media, to maximize engagement.

2. Product Development and Consumer Insights

   • Utilize data on regional trends and consumer preferences to guide product development and marketing strategies.
   • Collaborate with brand managers and designers to tailor collections or launch new offerings aligned with market demands.

3. Partnership Development and Retail Collaboration

   • Build relationships with retail chains, luxury brands, and supply chain leaders seeking strategic alliances.
   • Foster partnerships that expand distribution channels, enhance brand visibility, or improve operational efficiencies.

4. Market Research and Competitive Analysis

   • Analyze trends in Asia’s fashion industry to refine business strategies, identify market gaps, and anticipate consumer demands.
   • Benchmark against competitors to stay ahead in the fast-paced fashion landscape.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality fashion and apparel data at competitive prices, ensuring strong ROI for your marketing, sales, and product development efforts.

2. Seamless Integration

   • Integrate verified data into CRM systems, analytics platforms, or marketing tools via APIs or downloadable formats, streamlining workfl...

Success.ai’s Fashion & Apparel Data for Apparel, Fashion & Luxury Goods Professionals in North America offers a comprehensive dataset designed to help businesses connect with decision-makers and key professionals in the dynamic fashion and apparel industry. Covering roles such as designers, brand managers, retail executives, and supply chain leaders, this dataset provides verified contact details, professional insights, and actionable business data.

With access to over 700 million verified global profiles, including 130 million in North America, Success.ai ensures your marketing, outreach, and business development strategies are powered by accurate, continuously updated, and AI-validated information. Backed by our Best Price Guarantee, this solution is indispensable for thriving in North America’s competitive fashion market.

Why Choose Success.ai’s Fashion & Apparel Data?

1. Verified Contact Data for Targeted Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of professionals in apparel, fashion, and luxury goods industries.
   • AI-driven validation ensures 99% accuracy, improving communication efficiency and minimizing data gaps.

2. Comprehensive Coverage of North American Fashion Professionals

   • Includes profiles of professionals from major fashion hubs such as New York, Los Angeles, Chicago, and Miami.
   • Gain insights into regional trends, consumer preferences, and market opportunities in the fashion and luxury goods sectors.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership, brand expansions, and market dynamics.
   • Stay aligned with evolving industry trends and seize new opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful use of data.

Data Highlights:

• 700M+ Verified Global Profiles: Engage with apparel, fashion, and luxury goods professionals worldwide, with a focus on North America.
• 130M+ Profiles in North America: Gain verified contact details and actionable insights into the region’s top professionals.
• Contact Details: Access work emails, phone numbers, and business addresses for precision targeting.
• Leadership Insights: Connect with C-suite executives, brand managers, and designers driving innovation in the fashion industry.

Key Features of the Dataset:

1. Professional Profiles in Fashion and Apparel

   • Identify and connect with professionals responsible for product design, brand management, retail operations, and luxury goods marketing.
   • Target individuals leading creative initiatives, logistics, and digital transformation in the fashion industry.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (apparel, luxury goods, footwear), geographic location, or job function.
   • Tailor campaigns to align with specific market needs such as sustainable fashion, e-commerce, or retail expansion.

3. Regional Trends and Industry Insights

   • Leverage data on emerging fashion trends, consumer demands, and market growth in North America.
   • Refine marketing and product development strategies to align with audience expectations and market opportunities.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Brand Outreach

   • Design targeted campaigns to promote apparel, luxury goods, or supply chain solutions to professionals in the fashion industry.
   • Use verified contact data for multi-channel outreach, including email, phone, and social media.

2. Product Development and Innovation

   • Utilize insights into consumer preferences and fashion trends to guide product design and marketing strategies.
   • Collaborate with designers and brand managers to refine collections or launch new products.

3. Partnership Development and Collaboration

   • Build relationships with apparel brands, luxury retailers, and fashion designers exploring strategic alliances.
   • Foster partnerships that expand market reach, enhance brand visibility, or improve supply chain efficiency.

4. Market Research and Competitive Analysis

   • Analyze trends in North America’s fashion industry to refine business strategies, identify market gaps, and anticipate consumer demands.
   • Benchmark against competitors to stay ahead in the rapidly evolving fashion landscape.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality fashion and apparel data at competitive prices, ensuring strong ROI for your marketing, sales, and product development efforts.

2. Seamless Integration

   • Integrate verified fashion data into CRM systems, analytics platforms, or marketing tools via APIs or dow...

Global Articles of Apparel and Clothing Accessories of Furskin Market Size Value by Country, 2023 Discover more data with ReportLinker!

United States US: GDP: % of Manufacturing: Textiles and Clothing data was reported at 1.388 % in 2011. This records a decrease from the previous number of 1.440 % for 2010. United States US: GDP: % of Manufacturing: Textiles and Clothing data is updated yearly, averaging 5.330 % from Dec 1963 (Median) to 2011, with 47 observations. The data reached an all-time high of 8.446 % in 1963 and a record low of 1.388 % in 2011. United States US: GDP: % of Manufacturing: Textiles and Clothing data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Gross Domestic Product: Share of GDP. Value added in manufacturing is the sum of gross output less the value of intermediate inputs used in production for industries classified in ISIC major division D. Textiles and clothing correspond to ISIC divisions 17-19.; ; United Nations Industrial Development Organization, International Yearbook of Industrial Statistics.; ;

With the upgrading of consumption concepts, the fashion industry has huge market potential. According to statistics from authoritative organizations, the global market value of the fashion industry has exceeded US$3 trillion. At the same time, AI technology is also developing continuously, but the technology still faces many challenges in the process of integrating with the fashion industry. In order to promote the combination of AI technology and clothing fashion, Alibaba's "Image Harmony" team teamed up with the Department of Textiles and Clothing of the Hong Kong Polytechnic University to launch the industry's first large-scale high-quality fashion data set that meets both clothing professionalism and machine learning requirements, focusing on machines. There are two basic issues in cognitive fashion: clothing key point positioning and clothing attribute label identification. The clothing attribute label recognition data set was generated under this background. Clothing attribute tags are an important foundation for the clothing knowledge system, which is huge and complex. We have professionally organized and abstracted the clothing attributes, and built a label knowledge system that is consistent with the cognitive process, structured and meets the requirements of machine learning. The clothing attribute tag recognition technology born from this can be widely used in clothing image retrieval, tag navigation, clothing matching and other application scenarios. The image data is collected from Alibaba e-commerce data. This research topic focuses on local attribute identification of clothing products. All clearly identifiable attribute labels in the picture require prediction. Considering the complexity of clothing knowledge, this data set only retains the product image data of a single subject (single model or single piece tile), so that researchers can focus on solving the challenges in the attribute labeling task.

The revenue share in the sustainable segment of the apparel market worldwide was modeled to stand at 2.83 percent in 2017. Following a continuous upward trend, the revenue share has risen by 0.72 percentage points since 2013. Between 2017 and 2029, the revenue share will increase by three percentage points.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Apparel.

According to Cognitive Market Research, the global Casual Wear Market is driven by a major shift in consumer lifestyles, particularly the increasing preference for comfort and practicality over formality Market Dynamics of Casual Wear Market

The casual wear market is being driven by a major shift in consumer lifestyles, particularly the increasing preference for comfort and practicality over formality. The rise of remote and hybrid work environments has accelerated the demand for relaxed clothing that blends style with ease, such as joggers, oversized shirts, and casual denim. Younger consumers, especially Gen Z and millennials, are leading this shift, favoring self-expression, trend-driven designs, and gender-neutral options. The influence of social media, streetwear culture, and celebrity collaborations continues to shape consumer preferences, resulting in shorter fashion cycles and greater demand for new collections.

On the supply side, brands are focusing on fast production, flexible supply chains, and digital-first retail strategies to stay competitive. Sustainability is increasingly becoming a market driver, with consumers demanding eco-friendly fabrics, ethical sourcing, and transparent production practices. At the same time, the rise of direct-to-consumer (D2C) models and online marketplaces has lowered entry barriers for niche and emerging brands, intensifying market competition. Economic factors such as inflation and fluctuating raw material costs may pressure margins, but innovation in product design, customization, and value-oriented offerings continues to support overall market growth.

AI in Casual Wear Market

Artificial Intelligence (AI) is playing a transformative role in the casual wear market, enhancing both the consumer experience and backend operations. AI-powered trend forecasting tools analyze vast datasets from social media, fashion blogs, and e-commerce platforms to help brands design collections that align with real-time consumer preferences. Retailers are also using AI-driven recommendation engines and virtual try-on technologies to provide personalized shopping experiences, reduce return rates, and increase customer satisfaction especially important in casual wear, where fit and style vary widely. On the operational side, AI is streamlining supply chain management through demand forecasting, inventory optimization, and automated quality control. Casual wear brands are leveraging machine learning to better manage fast-changing fashion cycles, ensuring quicker response to market trends with minimal waste.

(Source:https://builtin.com/artificial-intelligence/ai-fashion) Introduction of Casual Wear Market

The casual wear market represents one of the largest and most dynamic segments of the global apparel industry, driven by shifting lifestyle preferences, the rise of remote work, and growing demand for comfort and versatility in clothing. This category includes a broad range of apparel such as t-shirts, jeans, hoodies, leggings, polos, and casual dresses, catering to men, women, and children across age groups. The market is heavily influenced by fast fashion trends, seasonal changes, and pop culture, with consumers seeking stylish yet functional attire for everyday wear. Increased focus on athleisure, sustainable fabrics, and digital shopping experiences is further reshaping the casual wear landscape, making it a key focus area for innovation and investment.

Global Not Sorted Used or New Rags Textile Material Market Size Value by Country, 2023 Discover more data with ReportLinker!

AI Trend Forecast SaaS (Fashion) Market Outlook

According to our latest research, the global AI Trend Forecast SaaS (Fashion) market size reached USD 2.14 billion in 2024, and it is expected to expand at a robust CAGR of 21.3% during the forecast period, reaching USD 14.25 billion by 2033. This remarkable growth is propelled by the increasing adoption of AI-driven solutions across the fashion industry, driven by the demand for accurate trend prediction, inventory optimization, and personalized consumer experiences. The integration of advanced analytics and machine learning algorithms into SaaS platforms has enabled fashion brands and retailers to anticipate market shifts, reduce operational inefficiencies, and elevate customer satisfaction, thereby fueling the sustained expansion of this dynamic market.

One of the primary growth factors for the AI Trend Forecast SaaS (Fashion) market is the escalating need for data-driven decision-making in the highly volatile fashion industry. As consumer preferences shift rapidly and competition intensifies, fashion brands and retailers are leveraging AI-based SaaS solutions to analyze vast datasets, including social media trends, sales data, and global fashion events. These platforms provide actionable insights that help businesses predict upcoming styles, optimize stock levels, and minimize markdowns. The ability to forecast trends with higher accuracy not only reduces excess inventory but also enhances profit margins, making AI-powered trend forecasting an indispensable tool for the modern fashion ecosystem.

The proliferation of e-commerce and digital channels has further accelerated the adoption of AI Trend Forecast SaaS solutions. With the fashion industry’s digital transformation, brands are increasingly seeking scalable, cloud-based platforms that offer real-time analytics and automated recommendations. These AI-enabled SaaS tools empower fashion companies to respond swiftly to emerging trends, tailor marketing campaigns, and personalize product offerings for diverse consumer segments. Moreover, the integration of visual recognition, natural language processing, and sentiment analysis technologies is enabling more nuanced understanding of consumer behavior, driving higher engagement and conversion rates across online and offline channels.

Another significant driver is the growing emphasis on sustainability and responsible consumption within the fashion sector. AI Trend Forecast SaaS platforms are being utilized to enhance supply chain transparency, reduce waste, and support eco-friendly product development. By accurately forecasting demand and aligning production accordingly, fashion brands can minimize overproduction and surplus inventory, which are major contributors to environmental degradation. The adoption of AI-driven forecasting solutions is thus aligned with broader industry initiatives aimed at achieving sustainability goals, enhancing brand reputation, and meeting the evolving expectations of environmentally conscious consumers.

Regionally, North America continues to hold the largest share of the AI Trend Forecast SaaS (Fashion) market, driven by the presence of leading technology providers, high digital literacy, and a mature fashion retail landscape. Europe follows closely, benefiting from a strong network of luxury fashion houses and increasing investment in AI-powered innovation. The Asia Pacific region is emerging as the fastest-growing market, supported by rapid urbanization, expanding e-commerce infrastructure, and the rising influence of tech-savvy consumers. As fashion brands across these regions prioritize agility and innovation, the demand for AI Trend Forecast SaaS solutions is expected to witness exponential growth, reshaping the global fashion industry landscape.

Component Analysis

The AI Trend Forecast SaaS (Fashion) market is segmented by component into software and services, each playing a pivotal role in shaping the industry’s future. The software segment encompasses AI-powered platforms that deliver advance

Success.ai’s Ecommerce Store Data for the APAC E-commerce Sector provides a reliable and accurate dataset tailored for businesses aiming to connect with e-commerce professionals and organizations across the Asia-Pacific region. Covering roles and businesses involved in online retail, marketplace management, logistics, and digital commerce, this dataset includes verified business profiles, decision-maker contact details, and actionable insights.

With access to continuously updated, AI-validated data and over 700 million global profiles, Success.ai ensures your outreach, market analysis, and partnership strategies are effective and data-driven. Backed by our Best Price Guarantee, this solution helps you excel in one of the world’s fastest-growing e-commerce markets.

Why Choose Success.ai’s Ecommerce Store Data?

1. Verified Profiles for Precision Engagement

   • Access verified profiles, business locations, employee counts, and decision-maker details for e-commerce businesses across APAC.
   • AI-driven validation ensures 99% accuracy, improving engagement rates and reducing outreach inefficiencies.

2. Comprehensive Coverage of the APAC E-commerce Sector

   • Includes businesses from major e-commerce hubs such as China, India, Japan, South Korea, Australia, and Southeast Asia.
   • Gain insights into regional e-commerce trends, digital transformation efforts, and logistics innovations.

3. Continuously Updated Datasets

   • Real-time updates ensure that business profiles, employee roles, and operational insights remain accurate and relevant.
   • Stay aligned with dynamic market conditions and emerging opportunities in the APAC region.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Access business profiles for e-commerce professionals and organizations across APAC.
• Firmographic Insights: Gain detailed information, including business locations, employee counts, and operational details.
• Decision-maker Profiles: Connect with key e-commerce leaders, managers, and strategists driving online retail innovation.
• Industry Trends: Understand emerging e-commerce trends, consumer behavior, and market dynamics in the APAC region.

Key Features of the Dataset:

1. Comprehensive E-commerce Business Profiles

   • Identify and connect with businesses specializing in online retail, marketplace management, and digital commerce logistics.
   • Target decision-makers involved in supply chain optimization, digital marketing, and platform development.

2. Advanced Filters for Precision Campaigns

   • Filter businesses and professionals by industry focus (fashion, electronics, grocery), geographic location, or employee size.
   • Tailor campaigns to address specific goals, such as promoting technology adoption, enhancing customer engagement, or expanding supply chains.

3. Regional and Sector-specific Insights

   • Leverage data on APAC’s fast-growing e-commerce markets, consumer purchasing trends, and regional challenges.
   • Refine your marketing strategies and outreach efforts to align with market priorities.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Outreach

   • Promote e-commerce solutions, logistics services, or digital commerce tools to businesses and professionals in the APAC region.
   • Use verified contact data for multi-channel outreach, including email, phone, and social media campaigns.

2. Partnership Development and Vendor Collaboration

   • Build relationships with e-commerce marketplaces, logistics providers, and payment solution companies seeking strategic partnerships.
   • Foster collaborations that drive operational efficiency, enhance customer experiences, or expand market reach.

3. Market Research and Competitive Analysis

   • Analyze regional e-commerce trends, consumer preferences, and logistics challenges to refine product offerings and business strategies.
   • Benchmark against competitors to identify growth opportunities and high-demand solutions.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers in the e-commerce industry recruiting for roles in operations, logistics, and digital marketing.
   • Provide workforce optimization platforms or training solutions tailored to the digital commerce sector.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality e-commerce store data at competitive prices, ensuring strong ROI for your marketing, sales, and strategic initiatives.

2. Seamless Integration

   • Integrate verified e-commerce data into CRM systems, analytics platforms, or market...

The apparel industry is considered one of the worst polluting industries, generating huge volumes of greenhouse gas emissions. It was calculated that in 2022, the apparel industry emitted approximately 879 million metric tons of carbon dioxide equivalents into the atmosphere. This is estimated to increase to over 1.2 billion metric tons by 2030 if no drastic action is taken. The source notes that the primary data necessary to give a completely accurate total is either incomplete or does not exist, and so these numbers are an estimate based on the data that is available. The fashion industry’s answer Many apparel brands are now taking steps to reduce their carbon footprint and increase transparency. For instance, H&M reported approximately 55,000 tonnes of Scope 1 and Scope 2 greenhouse gas emissions in the 2023 financial year, showcasing the industry's efforts to measure and disclose their environmental impact. Scope 1 refers to a company’s direct emissions such as fuel combustion. Scope to 2 refers to energy-related indirect emissions, and Scope 3 includes operational emissions associated with the value chain, e.g. transport and delivery processes. Sustainable fashion trends As consumers become more environmentally conscious on a global scale, the second-hand fashion market is expected to grow significantly. Projections indicate that the value of this market will increase by over 100 billion U.S. dollars from 2024 to 2028, reaching approximately 350 billion dollars.

Lithuania LT: Foreign Direct Investment Income: Outward: Total: Manufacture of Textiles and Wearing Apparel data was reported at -0.330 EUR mn in 2023. This records a decrease from the previous number of -0.190 EUR mn for 2022. Lithuania LT: Foreign Direct Investment Income: Outward: Total: Manufacture of Textiles and Wearing Apparel data is updated yearly, averaging -0.270 EUR mn from Dec 2006 (Median) to 2023, with 16 observations. The data reached an all-time high of 1.050 EUR mn in 2020 and a record low of -2.870 EUR mn in 2014. Lithuania LT: Foreign Direct Investment Income: Outward: Total: Manufacture of Textiles and Wearing Apparel data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Income: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

In 2023, 100 stakeholders across six continents participated in a consulation addressing developments on chemical usage and circularity within the fashion industry. Around 90 percent of the surveyed shared that they are currently working on designing all their textile products for the circular economy by 2040.

GVA: 2005-06p: Trend: Manufacturing: Textile, Clothing, Footwear & Leather data was reported at 709.000 AUD mn in Jun 2008. This records an increase from the previous number of 700.000 AUD mn for Mar 2008. GVA: 2005-06p: Trend: Manufacturing: Textile, Clothing, Footwear & Leather data is updated quarterly, averaging 1,580.500 AUD mn from Sep 1977 (Median) to Jun 2008, with 124 observations. The data reached an all-time high of 2,015.000 AUD mn in Dec 1988 and a record low of 696.000 AUD mn in Dec 2007. GVA: 2005-06p: Trend: Manufacturing: Textile, Clothing, Footwear & Leather data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.A356: SNA93: Gross Value Added: by Industry: Chain Linked: 2005-06 Price.

Lithuania LT: Foreign Direct Investment Income: Outward: Total: Manufacture of Textiles, Wearing Apparel, Wood and Paper Products: Printing and Reproduction data was reported at 0.600 EUR mn in 2023. This records an increase from the previous number of -2.620 EUR mn for 2022. Lithuania LT: Foreign Direct Investment Income: Outward: Total: Manufacture of Textiles, Wearing Apparel, Wood and Paper Products: Printing and Reproduction data is updated yearly, averaging -0.290 EUR mn from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 1.310 EUR mn in 2019 and a record low of -7.570 EUR mn in 2014. Lithuania LT: Foreign Direct Investment Income: Outward: Total: Manufacture of Textiles, Wearing Apparel, Wood and Paper Products: Printing and Reproduction data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Income: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Environmental impact is a significant issue for apparel, footwear, and jewelry businesses' performance according to a 2023 survey of such companies. Environmental management, product impact, and climate strategy were ranked as the top three material issues.

Lithuania LT: Foreign Direct Investment Position: Outward: Total: Manufacture of Textiles, Wearing Apparel, Wood and Paper Products: Printing and Reproduction data was reported at 31.640 EUR mn in 2023. This records an increase from the previous number of 16.630 EUR mn for 2022. Lithuania LT: Foreign Direct Investment Position: Outward: Total: Manufacture of Textiles, Wearing Apparel, Wood and Paper Products: Printing and Reproduction data is updated yearly, averaging 16.630 EUR mn from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 31.640 EUR mn in 2023 and a record low of 2.120 EUR mn in 2005. Lithuania LT: Foreign Direct Investment Position: Outward: Total: Manufacture of Textiles, Wearing Apparel, Wood and Paper Products: Printing and Reproduction data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Position: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Austria PP: OV: MM: Mfg of Wearing Apparel data was reported at 13.618 EUR mn in Apr 2020. This records a decrease from the previous number of 22.629 EUR mn for Mar 2020. Austria PP: OV: MM: Mfg of Wearing Apparel data is updated monthly, averaging 23.192 EUR mn from May 2019 (Median) to Apr 2020, with 12 observations. The data reached an all-time high of 26.688 EUR mn in Sep 2019 and a record low of 13.618 EUR mn in Apr 2020. Austria PP: OV: MM: Mfg of Wearing Apparel data remains active status in CEIC and is reported by Austrian Institute of Economic Research. The data is categorized under Global Database’s Austria – Table AT.B007: Output Value of Physical Production: by Industry.

According to a 2023 survey, the leading source of scope three emissions for fashion companies was upstream transportation and distribution. Purchased goods and services followed closely behind in second place. Business travel made up the top three most commonly cited sources. Carbon footprint of leading brands While Scope 3 emissions are the result of activities not controlled by a company, but affected indirectly by the company, Scope 1 deals with emissions from the company’s own sources, while Scope 2 deals with the indirect emissions where a company’s energy is produced. As pressure rises on fashion companies to report their activities transparently, more and more companies include sustainability figures in their annual reports. In 2023, Swedish retailer H&M surpassed other industry giants with roughly 55 thousand tons of Scope 1 and Scope 2 greenhouse gas emissions. Sustainable practices within fashion companies As consumers become more concerned with sustainability, to the point of it being a key factor in their purchases, fashion companies are listening and increasingly putting in effort to reduce their environmental impact. One method used to increase fashion sustainability is the adoption of circular fashion platforms like ThredUp and Depop, where consumers can buy and sell second-hand clothing.

Lithuania LT: Foreign Direct Investment Position: Outward: USD: Total: Manufacture of Textiles and Wearing Apparel data was reported at 4.762 USD mn in 2023. This records a decrease from the previous number of 6.847 USD mn for 2022. Lithuania LT: Foreign Direct Investment Position: Outward: USD: Total: Manufacture of Textiles and Wearing Apparel data is updated yearly, averaging 7.600 USD mn from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 10.178 USD mn in 2013 and a record low of 0.507 USD mn in 2005. Lithuania LT: Foreign Direct Investment Position: Outward: USD: Total: Manufacture of Textiles and Wearing Apparel data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Position: USD: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.